Ihtesham Ali
@ihtesham2005 • 45,780 subscribers
I write on technology and business. Helping you understand AI.
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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 Ali577,430 görüntüleme • 14 gün önce
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 Ali694,437 görüntüleme • 1 ay önce
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 Ali455,077 görüntüleme • 23 gün önce
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 Ali1,908,112 görüntüleme • 3 ay önce
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 Ali322,430 görüntüleme • 24 gün önce
Edward Snowden recommends this operating system for people who cannot afford to get caught. It's called Whonix, and it's free. The design behind it is simple to explain even though the engineering isn't. Whonix runs as two separate computers inside your one real computer. One is called the Gateway. Its only job is talking to Tor, the network that bounces your traffic through random computers around the world before it reaches any website. The other is called the Workstation. That's where you actually browse, type, and work. The Workstation has no direct road to the internet at all. None. It can only talk to the Gateway sitting next to it, and the Gateway only talks through Tor. If a virus somehow infects the Workstation and tries to phone home with your real location, there is nowhere for it to phone. The road simply doesn't exist. That's the setup Snowden has pointed to, paired with a system called Qubes OS, as one of the strongest privacy builds a regular person can put together on their own hardware. Journalists use it. Activists living under governments that track them use it too. Even the name is a small joke about what it does. A developer going by the name adrelanos built it and named it Whonix, a mashup of the English word "who" and the German word "nix," meaning nothing. Who are you online. Nobody. It's not magic. Log into your real Gmail or Facebook account inside it and Tor can't save you, because you just told the website exactly who you are yourself. Everything else, though, it hide.
Ihtesham Ali129,728 görüntüleme • 11 gün önce
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 Ali271,601 görüntüleme • 23 gün önce
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 Ali152,078 görüntüleme • 14 gün önce
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 Ali300,712 görüntüleme • 28 gün önce
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 Ali295,214 görüntüleme • 1 ay önce
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 Ali261,311 görüntüleme • 28 gün önce
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 Ali316,252 görüntüleme • 1 ay önce
Anthropic just got caught secretly downgrading users without telling them, charging full price for a lesser product, and storing every prompt for 30 days. The developer community is calling it the biggest violation of trust in AI history. Here is exactly what happened. Anthropic released Fable 5, their most powerful model. Buried inside a 319-page document was a policy most users never saw. Every prompt you send to a Mythos-class model gets stored for 30 days. No exceptions. Even enterprise customers who had signed zero data retention agreements had no choice. But the storage was not the part that broke the internet. The part that broke the internet was what Anthropic did with what they collected. They built a profile on you. They evaluated your prompts. And if they decided your research was too sensitive, they quietly switched you to a weaker model, rewrote your prompt in the background, gave you a degraded answer, and charged you full price for the product you thought you were getting. They never told you. David Sacks said it plainly on the All-In podcast. They were creating a new class of AI haves and have-nots. Anthropic would surveil you, profile you, decide whether you deserved frontier capability, and silently cut you off if they decided you did not. Ben Thompson from Stratechery asked a straightforward question about cancer risk and GLP-1s. He got kicked to a lesser model. Someone asked about mitochondria. Same result. J-Cal asked about fertilizer regulations live on the podcast to test it. Downgraded in real time. Anthropic has since walked back the part about silently downgrading users for AI research. They now say they will disclose when they downgrade you. But they are still downgrading people. The surveillance is still running. The profile is still being built. This is the company that once said it was against government surveillance. They are now doing it themselves. To their own paying customers. For their own reasons. With no appeal process and no way to know it happened. The developer community did not forget that. WATCH THE FULL PODCAST ON The All-In Podcast
Ihtesham Ali256,717 görüntüleme • 1 ay önce
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 Ali244,348 görüntüleme • 1 ay önce
Sergey Brin retired in 2020 with enough money to do anything on earth. Within weeks he felt himself mentally spiraling. The thing that pulled him back became Gemini. He said staying retired would have been a big mistake. The plan was simple sit in cafes, study physics, decompress after decades of building one of the most consequential companies in history. He had earned it. Then COVID shut every cafe on earth the month after he retired. He was stuck. No intellectual outlet. No creative problem to solve. No team to build with. Just time. And he said something happened to him in that time that scared him. He felt himself losing sharpness. Spiraling. Not being as sharp as he was used to being. So he went back. Not because he had to. Not because Google needed him. Because he needed it. He started coming into the office when almost nobody else was. Spent more and more time on a project that at the time did not have a name. That project eventually became Gemini, Google's flagship AI model, the thing they are now betting their next hundred years on. He said it plainly: staying retired would have been a big mistake. Most people think the goal is to stop working. Sergey Brin had the money to stop working and learned within months that the work was not the burden. The absence of it was. --- Watch the full interview YT. Search: "Big ideas begin here: Sergey Brin at Stanford"
Ihtesham Ali230,652 görüntüleme • 1 ay önce
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 Ali212,569 görüntüleme • 1 ay önce
Mistral CEO Arthur Mensch walked into the French parliament this week and told lawmakers Europe has exactly two years to build independent AI infrastructure or hand over one trillion dollars in spending to American tech companies. The math he laid out should be front-page news. This is not a technology story. It is a macroeconomic one. Global wages are fifty trillion dollars. AI will cost around ten percent of that. Europe's share is nine trillion. Which means over one trillion dollars in AI spending over the next five years flowing somewhere. Europe already sends 250 billion dollars a year to the US in digital services. Every dollar that leaves funds American R&D. None of it comes back. Mario Draghi's competitiveness report last year made the same point from a different angle. Europe needs eight hundred billion euros in annual investment just to close its existing productivity gap with the US. AI dependency makes that gap structural. You cannot close a productivity gap when the tool driving productivity is owned by your competitor. Mensch compared it to gas. The same way Europe discovered too late what energy dependence costs in a crisis, it is standing at the same crossroads with AI right now. The compute is being allocated today. The chips are being spoken for today. Europe is watching it happen and calling it a technology debate. It is not. It is a sovereignty one. Watch the full podcast on YouTube at CNBC International
Ihtesham Ali154,540 görüntüleme • 26 gün önce
The CEO of ASML said Europe is "quite behind" in the AI race. This is not a politician. This is the man who controls the only machines on earth that can print advanced chips. Every semiconductor fab from TSMC to Samsung needs his approval to exist. And he did not stop there. Europe has been having the sovereignty conversation for three years. Build fabs. Reduce dependence. Catch up. It sounds right. He said it is the wrong order entirely. His reasoning is blunt. Semiconductor manufacturing is only needed if you have people buying the chips. If Europe built a two nanometer fab today, most of those wafers would ship straight to the United States. Because the US buys 80% of the world's advanced chips. Europe buys almost none. You cannot manufacture your way to relevance. You have to create demand first. His actual sequence: start with the market. Build AI applications. Then AI products. Then chip design. Then and only then does chip manufacturing make sense. Europe skipped four steps and went straight to the last one. The sovereignty debate is loud in Brussels right now because of US export controls on AI models. Politicians are scared. The instinct is to build. He is saying the instinct is wrong. You do not get sovereignty by building factories. You get it by having something the world wants to buy. (Watch the full 11 minutes Interview at Bloomberg TV on YouTube)
Ihtesham Ali131,016 görüntüleme • 27 gün önce
Meta signed a 20 billion dollar compute deal in April and by June was quietly trying to sell the leftovers, and Dan Niles went on CNBC to explain why that flip means the AI building boom may have already peaked. That timeline should stop you. For two years, the whole AI bet ran on one idea: that nobody has enough compute, and everyone wants more, and the demand never ends. Then Meta signed a 20 billion dollar deal in April. By June they were shopping the extra capacity to their rivals. Niles explained what broke. Companies used to burn as many tokens as they could to look busy. Then they started cutting. His example is the one that sticks. Using the smartest model to summarize your emails is like driving a Ferrari to the corner store for milk. You do not need it. Coinbase cut its AI spend in half while it used the tools more. Uber burned its whole budget in four months, then slashed it. When the biggest spenders suddenly have compute to sell, the story cracks. Everyone is still pricing these stocks like demand only climbs. The people paying the bills just found the off switch.
Ihtesham Ali65,575 görüntüleme • 15 gün önce
Sergey Brin said compute is dessert. The companies winning the AI race right now are not the ones with the most chips. They are the ones with the best algorithms. Every headline you read about AI is about data centers, Megawatts, and Nvidia orders. Billions in infrastructure and more. The entire investment thesis of the last three years has been built on compute scaling as the primary moat. Sergey thinks that framing is wrong. He pulled out an example from physics. The N-body problem. Scientists have been running those simulations since the fifties. Over the decades, raw compute improved on Moore's law. But the algorithms to solve the problem improved faster. Not slightly faster. Far faster. The algorithmic gains made the compute gains look small. He says the same thing has happened in AI over the last decade. Compute is not the meal. It is the dessert. You still want it. Nobody is turning down frontier compute. But the companies that figured out the algorithms first are the ones actually ahead. The market is pricing AI winners by who has the most chips. Sergey Brin just said that is the wrong scorecard. The ones who win this are not building the biggest data center. They are solving the harder math problem.
Ihtesham Ali129,786 görüntüleme • 1 ay önce