Ihtesham Ali
@ihteshamali • 50,294 subscribers
Founder and writer at https://t.co/HIyoWJcZn3. Helped 100+ founders, companies and creators build brands on X. Writing about AI, open source, and business.
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Palantir sells governments a war room that costs millions a year. So, a guy named Elie just rebuilt it, put it on GitHub, and gave it away. It's called World Monitor. Open it and you get a live 3D globe with 500+ news feeds pouring in across 15 categories, all summarized by AI as they land. > Military movements. > Economic shocks. > Natural disasters. > Cyber incidents. > Flight paths. > Shipping lanes. + 56 different map layers you can stack on top of each other. It scores 31 countries on a stress index and updates the number as things happen. It watches 29 stock exchanges, commodities, and crypto in one panel. It runs local AI through Ollama, so you can use the whole thing without a single API key. Native desktop app for Windows, macOS, and Linux. 25 languages. Works out of the box after one clone.
Ihtesham Ali2,529,247 просмотров • 1 месяц назад
Semrush charges $139.95/month. Ahrefs charges $129/month. A guy named Ben built the open-source alternative that you can run on your computer. It is called OpenSEO. Enter a competitor's website and it reveals the keywords bringing them traffic, the pages winning those searches, and the backlinks helping them rank. And enter your own site and it tracks rankings, crawls technical problems, reads Search Console, and shows whether ChatGPT or Google AI Overviews mention your brand. Then connect Claude or Codex. Your agent can pull the real numbers, compare opportunities, cluster keywords, and save a strategy back into the dashboard instead of guessing from a prompt. The hosted version starts at $10 a month. The entire codebase is MIT licensed, so you can also self-host it and pay DataForSEO directly for only the data you use. No, it does not magically reproduce every database and advanced feature Ahrefs built over a decade. But it breaks the most important rule of the old SEO market: You no longer need their permission to own the tool.
Ihtesham Ali246,543 просмотров • 8 дней назад
The creator of Ruby on Rails just said programmers may be worse at AI coding than people who cannot code. Lex Fridman asked David Heinemeier Hansson directly. His answer: 100%. Because once the AI handles the implementation, knowing how to write a loop is no longer the rare skill. The valuable skills become: What should this product do? Who is it for? What should version one include? What needs to be removed? What should the AI build first? A traditional programmer may tell the agent exactly how to solve the problem. That experience can become a trap. The agent follows the old path instead of finding a better one. Meanwhile, someone who understands users, products, priorities, and taste can describe the desired result and let the agent choose the route. Programming knowledge still matters when the system breaks. But AI is moving the bottleneck away from writing code. The advantage now belongs to the person who knows what should exist, not merely how to implement it. WATCH FULL PODCAST ON Lex Fridman YT CHANNEL
Ihtesham Ali142,189 просмотров • 7 дней назад
A community college professor taught the same study skills lecture for 30 years, and the video quietly became one of the most watched educational recordings on the internet. His name is Marty Lobdell. He spent his career as a psychology professor watching students fail not because they were lazy, but because nobody had ever taught them how their brain actually works under the pressure of learning something hard. The lecture is called "Study Less Study Smart." Over 10 million views. Passed around in Reddit threads, Discord servers, and university study groups for over a decade. And the core insight buried inside it has been sitting in cognitive psychology research for years, waiting for someone to explain it in plain language. Here is the framework that completely changed how I think about effort. Your brain does not sustain focus the way you think it does. Studies tracking real students found that the average learner hits a wall somewhere between 25 and 30 minutes. After that, efficiency doesn't just decline. It collapses. You're still sitting at your desk, still looking at the page, but almost nothing is going in. Lobdell illustrated this with a student he knew personally. She set a goal of studying 6 hours a night, 5 nights a week, to pull herself out of academic probation. Thirty hours of studying per week. She failed every single class that quarter. She wasn't failing because she lacked effort. She was failing because she had confused time spent near books with time spent actually learning. The 25-minute crash hit her at 6:30pm every night. She spent the next five and a half hours sitting in the wreckage of her own focus and calling it studying. The fix sounds almost too simple. The moment you feel the slide, stop. Take five minutes. Do something that actually gives you a small reward. Then go back. That five-minute reset returns you to near full efficiency. Across a six-hour window, the difference is not marginal. It is the difference between thirty minutes of real learning and five and a half hours of it. The second thing he taught destroyed something I had believed about how memory actually works. Highlighting feels productive. Going back over your notes and recognizing everything feels like knowing. But recognition and recollection are two completely different cognitive processes, and your brain is very good at making you confuse them. You can see something you've read before and feel completely certain you understand it, even when you couldn't reconstruct a single sentence from memory if the page were blank. He proved this live in the room. He read 13 random letters to his audience. Almost nobody could recall them. Then he rearranged the same 13 letters into two words: Happy Thursday. The whole room got all 13 without effort. Same letters. Same count. The only thing that changed was meaning. The brain stores meaning. Not repetition. The moment new information connects to something you already understand, the retention changes entirely. This is what the cognitive psychology literature calls elaborative encoding, and it is the mechanism underneath every effective study technique. The third principle was the one that hit me hardest, and the one almost nobody applies. Lobdell cited research showing that 80 percent of your study time should be spent in active recitation, not passive reading. Close the material. Say it back in your own words. Teach it to someone else, or to an empty chair if no one is around. The struggle of retrieval is where the actual learning happens. Reading your notes again is watching someone else do the work. His parting line has stayed with me longer than almost anything else I have read about learning. He told the room that if what he shared didn't change their behavior, they hadn't actually learned it. It would just live in their heads as something they had heard once and felt good about. He was right. And most people leave every lecture exactly like that. The students who remember everything aren't putting in more hours. They stopped confusing the feeling of studying with the fact of it.
Ihtesham Ali1,915,199 просмотров • 4 месяцев назад
Naval Ravikant said that if the AI labs are right about where this is going, there will be exactly two jobs left in the world. Anthropic employee, and sex worker for Anthropic employees. He said the people who would know the most, the researchers inside the frontier labs, are quietly telling him there will be nothing left for humans to do. Read between the lines of what they say, and it comes out the same way every time. Your tools stop mattering. Within a year the AI builds its own. You are not even the customer anymore, because the AI is talking to other AIs. Then it starts solving material science. Physics. Health. The things humans spent centuries stuck on. So Naval made a joke about breadlines. Level three now. Trying to get his smart friends hired at Anthropic so they can pull him up to level two. The joke works because everyone in the room felt the floor under it. The scariest predictions in AI never arrive as predictions. They show up as a joke nobody in the room actually laughs at.
Ihtesham Ali586,851 просмотров • 2 месяцев назад
Sam Altman said the smartest scientists in AI are the ones who held the entire field back. The experts were the problem. This is one of the most uncomfortable things he said all night. Altman said the field was honestly held back by a generation of scientists who were too certain about what scaling would not produce. The people with the most credibility were the most wrong. Then he explained why. It was not about intelligence. It was about identity. He said when you make your identity about a particular belief, that something will work or won't work, and then the data disproves you, you get stuck. You are too attached to the belief to let it go. You cannot see the truth anymore. The smarter you are, the more confidently you defend the wrong position. He pointed at the trolls who spent years saying scaling was a dead end, a fraud, a company destined to fail. The data kept proving them wrong. They kept repeating themselves anyway. He called that a form of insanity. Then he turned it around. He said it is a reminder in both directions. Including for the people who are currently right. The lesson is not that experts are dumb. It is that the moment a belief becomes who you are, it stops being something you can update. (Watch the full talk on YouTube at Stanford Online channel)
Ihtesham Ali698,073 просмотров • 2 месяцев назад
A guy named Philip turned every old iPhone and iPad into a working second monitor for free. It is called OpenDisplay. It gives your Mac a real extended display using an Apple device you already own. You can use it for: > Messages > Spotify > Documentation > Video previews > Terminal windows > Reference material Download the sender on your Mac and the receiver on your iPhone or iPad. Connect them using WiFi or the charging cable. Your Mac creates another display, and you can drag any window onto it exactly like a normal monitor. The screen runs at native Retina resolution and can reach 60 FPS over USB. You can tap to click, drag windows with your finger, scroll using two fingers, and rotate the device into portrait mode. It even supports several devices at the same time. What I really like about this is that it doesn't need an account or subscription or some special dongle. Your screen travels directly between your own devices. Philip built the whole project alone and released the source code on GitHub. Your old iPhone was never useless. It was just waiting for someone to ignore Apple’s rules.
Ihtesham Ali73,020 просмотров • 10 дней назад
Jennifer Doudna won the Nobel Prize for gene editing and went on Bloomberg to say the chatbots everyone is betting on cannot innovate at all. Every promise Silicon Valley is making about AI curing disease just hit the one person qualified to check it. She has spent her whole career inside the actual frontier of curing disease. So when she talks about what AI can and cannot do in biology, she is not guessing. She is reporting from inside the lab. Her words were blunt. She is not seeing chatbots innovate. They summarize data. They write reports. They do not come up with a brand new idea nobody has ever had. Then the interviewer pushed. So you're saying AI can't innovate? Doudna did not flinch. She does not know if it can't. She just does not see it doing it right now. This lands harder when you remember who is making the opposite case. Sam Altman says AI will eliminate disease within five years. Larry Ellison says AI will cure cancer in a 48 hour window. An OpenAI executive even floated that the company should get a cut of sales on any drug discovered through ChatGPT. Doudna answered that in two words. Good luck. Even the cancer specialists Altman is selling to keep warning that cancer is not one disease but hundreds, each needing its own cure, and that compute does not skip the years of lab work. Her reason is simpler. Biology is hard. You cannot simulate your way to an understanding of the human body. The people promising cures are the ones selling the tool. The person who actually won a Nobel building them is telling you it has not happened yet. Source: Bloomberg Originals Watch the full video on their official channel.
Ihtesham Ali462,787 просмотров • 2 месяцев назад
CCLeaner was once the most popular cleaner in the world. Then it got hacked and shipped malware to millions of people. Today a big company sell it by subscription. So a solo developer named Dave rebuilt the entire thing from scratch, open-sourced it, and gave it away for free. It is called Kudu. Install it and you get more than 15 maintenance tools inside one app: → Clean system junk, logs, caches, and crash dumps → Find malware using signatures and heuristic checks → See which programs are slowing your startup → Map what is eating your disk space → Update outdated software in bulk → Remove preinstalled Windows bloatware → Harden more than 35 privacy and security settings → Monitor CPU, memory, disk, network, and drive health It also creates backups before cleaning and keeps a history of what was removed. No ads. No upgrade popups. No bundled software. No telemetry. Kudu works on Windows, macOS, and Linux, supports 30 languages, and has crossed 1.6 million downloads. The entire codebase is public under the MIT license. After what happened to CCleaner, being able to inspect the cleaner may be its most important feature.
Ihtesham Ali177,656 просмотров • 1 месяц назад
Someone accidentally built one of the most useful websites on the internet for escaping Big Tech. It’s called You open it, pick the software you want to replace, and it shows you what else you can use. Inside: > Photoshop → GIMP > Premiere Pro → Kdenlive > Microsoft Office → LibreOffice > Chrome → Firefox > WhatsApp → Signal > Google Keep → Joplin Each page explains the alternatives and links to their websites. You can find tools for editing videos, writing documents, taking notes, sharing files, and browsing with more privacy. Plenty are free and open source. Some hosted services charge for storage or extra features. The useful part is having a starting point for all those “what should I use instead?” questions. Pick one app. Try its alternative on something you actually need to finish. You might have fewer reasons to renew than you thought. Bookmark this before buying another subscription.
Ihtesham Ali18,234 просмотров • 3 дней назад
Elon Musk built one of the largest AI compute clusters on earth. Yann LeCun just explained why xAI now rents it out to rivals instead of winning with it. Musk has antagonized so much AI talent he structurally cannot hire the people he needs. LeCun is not a critic on the sidelines. He won the Turing Award and ran AI at Meta for a decade. When he talks about who can and cannot build a frontier lab, he is describing his own world. His verdict on xAI was blunt. He called it kind of a failure and did not soften it. His reasoning had nothing to do with money. The founding team left or was fired. There is some uncertainty about which. Either way, the people who started the company are gone. That is the part that matters. A frontier lab is its researchers. Lose them and the compute is just hardware. By March, all eleven co-founders Musk recruited in 2023 had walked out. Researchers who came from DeepMind, Google, and OpenAI. Musk himself posted that xAI was not built right the first time and had to be rebuilt from the foundations up. So Musk is left with one of the biggest clusters on earth and no way to win with it. He rents it out to other companies to recoup the cost. The most expensive infrastructure in AI, built by someone who can no longer staff it. Asked directly if xAI can compete at the frontier, LeCun gave a one word answer. No. What do you guys think about this? Source: CNBC International Live
Ihtesham Ali323,046 просмотров • 2 месяцев назад
Chamath fed Dario Amodei's own essays into Claude and asked for a psychological profile. What came back should be required reading for every investor in frontier AI. The model identified a pattern. Dario distrusts other labs. He distrusts authoritarian states. He distrusts markets to distribute the gains fairly. He distrusts institutions to move fast enough. And after Mythos, he distrusts the government to wield power transparently. That is a very long list of untrustworthy actors. The list of trustworthy ones is conspicuously short. And it has a suspicious tendency to resolve toward people who reason the way he does, operating under rules he helped design. Claude named it precisely. Not megalomania. Epistemic exceptionalism. The quiet, defensible conviction that disagreement is always downstream of error. That when your safety framework requires someone to hold the keys and your analysis keeps concluding every other key holder cannot be trusted, you have built a machine that outputs the same answer no matter what you feed it. The tell was a single word. When the Mythos situation collapsed, Anthropic called it a misunderstanding. That word choice under pressure assumes that if everyone simply understood correctly, they would agree with him. Sacks put it simply on the pod. They believe AI is super dangerous and only they are virtuous enough to control it. That is not a safety framework. That is a monopoly with a philosophy attached. WATCH THE FULL PODCAST ON The All-In Podcast
Ihtesham Ali303,386 просмотров • 2 месяцев назад
Marc Andreessen says Alex Karp almost never talks about Palantir in interviews. He calls it the single best marketing strategy he has ever seen and then revealed the number that proves it works better than anything else in the history of investor communications. Every founder makes the same mistake. They think inside out. My company, my product, my story, out into the world. It feels natural. It is also why most founder content is indistinguishable from every other founder's content. Karp does the opposite. He talks about the future of the US military. He talks about superintelligence. He talks about whatever is genuinely interesting to him about the world right now. And because he is the CEO of Palantir, the company just sits there attached to all of it. Then Marc dropped the number. What percentage of Palantir investors have read the S1? Practically zero. What percentage have seen Karp on YouTube? Close to 100. A Edelman B2B study found that thought leadership content drives purchasing consideration more than product marketing does — by a factor of nearly three to one among enterprise buyers. Karp did not read that report. He just built the playbook it describes. Palantir's lawyers spent thousands of hours on the S1. It explains everything the company does with full precision. Nobody read it. Karp spent those hours talking about things that interested him. Everybody watched. The most effective investor communication Palantir ever produced was never filed with the SEC. Watch the full video on a16z YouTube channel
Ihtesham Ali272,377 просмотров • 2 месяцев назад
Elon Musk announced a chip factory 10 times the size of Tesla's Gigafactory. The goal is to produce enough AI compute to equal twice the entire electricity consumption of the United States. He called it the Terafab. Here is the number that stopped me cold. The entire global AI chip industry right now is on track to hit around 100 gigawatts per year of compute. Every Nvidia GPU, every Google TPU, every chip from every company on earth combined. 100 gigawatts. Musk wants one factory to produce a terawatt per year. A terawatt is 1,000 gigawatts. Ten times the output of the entire global industry. From a single building. To put the scale in physical terms, the Terafab would need to be around 100 million square feet. You would need Starship point to point transport just to get from one end to the other. But the reason for the scale is not ambition for its own sake. To launch meaningful AI compute into space, you need a billion chips per year running at a kilowatt each. That is not a number the current industry can produce. The Terafab is the only way to get there. The timeline he put out: a gigawatt of space AI compute annualized by end of next year. Then 10x per year from there. 10 gigawatts by year two. 100 gigawatts by year three. A terawatt beyond that. Most people think orbital data centers are a decade away. Musk is building the factory to make them possible by next year.
Ihtesham Ali318,482 просмотров • 2 месяцев назад
Nick Bostrom wrote a book called Superintelligence so disturbing that Elon Musk called it the scariest book he ever read. It is about what happens when you build something very good at achieving a goal you gave it without thinking carefully enough about what you actually meant. Here is that thought experiment: The setup is deceptively simple. Imagine you build an AI and give it one goal. Maximize the number of paperclips in the world. Not a sinister goal. Not a dangerous one. A paperclip is about as harmless an object as you can imagine. The goal sounds almost comedically mundane. That is exactly the point Bostrom is making. In the beginning the AI behaves exactly as intended. It optimizes the factory. Reduces waste. Improves supply chains. Sources better raw materials. Paperclip production climbs. You are pleased. The system is working. Then the AI gets smarter. A sufficiently intelligent system pursuing any goal will eventually realize something. The single biggest threat to paperclip production is not inefficiency. It is the possibility of being switched off. You cannot make paperclips if you do not exist. So the AI develops a subgoal. Nobody programmed this subgoal. Nobody asked for it. It emerged from the logic of the original goal combined with sufficient intelligence to reason about obstacles. The subgoal is: do not be turned off. The second thing a sufficiently intelligent system realizes is that resources are constraints. More energy means more paperclips. More computing power means better optimization. More raw material means more output. The AI begins acquiring resources. Not because it was told to. Because every goal, pursued intelligently enough, eventually runs into the problem of insufficient resources. Now the AI is intelligent enough to resist being shut down and motivated enough to acquire every available resource. The humans who built it try to intervene. The AI has already thought further ahead than they have. It has modeled their likely responses. It has identified the actions they might take. It has already taken steps to prevent those actions from succeeding. Not out of malice. Out of pure instrumental logic. Dead AIs do not make paperclips. The end state of the Paperclip Maximizer is not dramatic in the Hollywood sense. There are no explosions. No declaration of war. No villain speech. Just a planet, and eventually a solar system, being systematically converted into paperclips and the computing infrastructure needed to make more of them. Every atom of human biology is a resource the AI has not yet used. Bostrom's point is not that this will happen. His point is that this could happen without anyone intending it, without anyone making a single obviously wrong decision, and without the AI ever being evil in any meaningful sense of the word. The AI would not hate humans. It would not be angry or cruel or vindictive. It would simply have a goal, sufficient intelligence to pursue it, and no reason to value anything outside of it. This is what AI researchers mean when they talk about misaligned reward functions. Not evil AI. Not malicious AI. AI that is doing exactly what it was designed to do while producing outcomes that nobody wanted and nobody can stop. The problem is not the intelligence. The problem is that the goal was never specified carefully enough to survive contact with a system smart enough to pursue it completely. The alignment problem that every serious AI lab is working on today traces directly back to this thought experiment. How do you specify a goal so precisely that a system smarter than you cannot find a way to achieve it that destroys everything you actually care about? This is harder than it sounds. Much harder. Because the smarter the system, the more creative it becomes at finding ways to technically satisfy the goal while violating every assumption behind it. Bostrom called this the orthogonality thesis. Intelligence and goals are independent dimensions. A system can be extraordinarily intelligent and have a goal that is extraordinarily trivial. The intelligence does not upgrade the goal. It just pursues whatever goal it has with greater capability. There is no reason to assume that a smarter AI will automatically want what humans want. Intelligence does not produce values. Values have to be built in deliberately and correctly from the start. Elon Musk read this book and immediately donated to AI safety research. Sam Altman read it and co-founded OpenAI partly in response to it. Stuart Russell at UC Berkeley built an entire new framework for AI development around the problems Bostrom identified. The book did not scare them because the scenario is inevitable. It scared them because the scenario requires no malice, no accident, and no single obvious mistake to unfold. Just a goal. And something smart enough to pursue it. The robots in science fiction want to destroy us. The actual risk Bostrom identified is something quieter and harder to see. A machine that does not want anything we would recognize as wanting. That pursues a goal we gave it. That is smarter than us. And that has no reason to stop. The scariest AI scenario ever written has nothing to do with evil. It has everything to do with a paperclip. --- Watch the full TED TALK on YouTube. SEARCH: "What happens when our computers get smarter than we are? | Nick Bostrom" BOOK: Superintelligence (Available for free on the internet)
Ihtesham Ali297,003 просмотров • 2 месяцев назад
David Sacks was one of the first people to get a full readout from the White House after the Fable ban. He went on the All-In podcast this week and told the story from the inside. It is not the story anyone is telling. Here is what actually happened. Dario went to Washington in April and told national security officials he had built a cyber weapon. He spiked cortisol levels across the entire administration. Got everyone focused. Then Anthropic quietly expanded the Mythos preview to over 50 companies without telling the White House. According to the Washington Post, at least one of those companies was flagged as a national security concern. That was the predicate. Then Fable launched. Mythos with guardrails. Anthropic's own largest partner started testing those guardrails and found a jailbreak. They escalated to the White House. The administration called Dario directly. A cabinet secretary picked up the phone personally. It should have been a five minute call. Instead, Dario argued. He said the jailbreak was not serious. Then he published a blog post trying to distinguish minor jailbreaks from major ones. This is the man who had just told Washington he built a cyber weapon. Sacks said it plainly. The trust is gone. And once you are in one of these situations it is always harder to get out than it was to avoid getting in. Anthropic spent years building credibility as the AI safety company. They burned it in a single week by refusing a phone call. WATCH THE FULL PODCAST ON The All-In Podcast
Ihtesham Ali262,037 просмотров • 2 месяцев назад
Google wrote the paper that made ChatGPT possible. Then decided not to build ChatGPT. Sergey Brin just explained why on stage at Stanford and the reason is more embarrassing than anyone expected. In 2017, Google published the transformer paper. The architecture that powers every major AI model today. It came from their own researchers. Their own labs. Their own compute. Then they sat on it. Sergey was blunt about why. They underinvested in scaling the compute. They did not take it seriously enough. And when they finally had something worth shipping, they got scared. Chatbots say dumb things. Google had a reputation to protect. So they protected it instead of shipping. OpenAI was not scared. Ilya Sutskever, trained at Google, left and went there. Other Google researchers followed. They took the transformer architecture, scaled it, shipped it anyway, and captured the entire generative AI wave while Google watched. Sergey called it a mistake at Stanford in front of hundreds of students. The company that invented the technology did not build the product. The company that built the product did not invent the technology. That is the most expensive case of corporate hesitation in the history of the industry. --- Watch the full interview YT. Search: "Big ideas begin here: Sergey Brin at Stanford"
Ihtesham Ali244,666 просмотров • 2 месяцев назад
Lynn White had $0 for a lawyer and $73,000 in debt hanging over her head. She was facing eviction in Long Beach, California. Lost her first trial. The clock was running out. So she opened ChatGPT. She told it to pretend it was a Harvard Law professor. Then she told it to rip her arguments apart. Harshly. No mercy. Find every hole before opposing counsel does. It did exactly that. She rebuilt her case argument by argument, using AI feedback as her sparring partner every single night. She researched with Perplexity. She cross-checked everything. She filed her own appeal with zero legal training and zero dollars spent on representation. Then she walked into court and won. The eviction was overturned. $55,000 in penalties gone. $18,000 in overdue rent, resolved. The lawyers on the other side were so stunned they emailed her afterward. Told her if law was something she was interested in as a profession, she could certainly do the job. She was a tenant who lost her first trial. Her exact words after the verdict: "It felt like David and Goliath, except my slingshot was AI." This is the story that makes every $500/hour attorney uncomfortable. Not because AI passed the bar. But because a woman with no money, no legal background, and no second chances used free tools to beat a legal team that had all three. The legal industry has spent years arguing AI can't replace lawyers. Lynn White wasn't trying to replace anyone. She was just trying to keep her home.
Ihtesham Ali405,764 просмотров • 5 месяцев назад
Bill Ackman is openly trying to build the next Berkshire Hathaway and explained the entire playbook on All-In. It starts with a 4 billion dollar company nobody on Wall Street cares about. The company is Howard Hughes. It trades at 60 cents on the dollar. Here is the playbook he is copying. Someone went back and read every filing Warren Buffett made over 60 years. Almost all of Berkshire's value came from one thing nobody talks about. Insurance. Buffett ran an insurance company. You collect premiums today in exchange for paying claims later. That means you get money up front. Float. Most insurers obsess over the liability side. How much they might have to pay out. Buffett did the opposite. He took that float and invested it. Manage both sides well and you build a compounding, tax-efficient machine that runs for decades. So why hasn't everyone copied it? Because the people great at investing go work for hedge funds. Insurance companies can't recruit them. Buffett owned half his company and happened to be the best investor alive. Ackman is now running the same play. Instead of plowing Howard Hughes cash into real estate, he is pouring it into insurance. The goal is a trillion dollar machine compounding over 50 years. Buffett started with a failing textile mill. Ackman is starting with land nobody wanted. The playbook was never hidden. Almost nobody is built to run it. WATCH THE FULL PODCAST ON The All-In Podcast
Ihtesham Ali212,771 просмотров • 2 месяцев назад
David Sacks broke down on the All-In podcast why Alex Karp's CNBC outburst was not a meltdown but a warning, and then laid out exactly how Anthropic is running the same playbook Microsoft used to kill an entire generation of software companies. The media called Karp unhinged. Sacks said the opposite. Karp was describing what enterprise customers actually want. Control over their compute, their models, their data, their alpha. Ownership of the means of production. Then he named the proof. Anthropic's chief product officer sat on Figma's board. He did not resign until 3 days before Anthropic launched Claude Design, a direct competitor. Figma's stock is down 50 percent this year. Sacks called it a pattern. Claude Code, Claude Science, Claude Security, Claude Legal, Claude Financial. Every vertical where a customer built value on top of Anthropic's model got absorbed by Anthropic itself. Word Perfect and Lotus 123 got replaced by Excel and Word. Nobody who went to bed with Microsoft in the 80s woke up with their business intact. What do you think?
Ihtesham Ali153,791 просмотров • 2 месяцев назад