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SITUATION EXPLAINED: Paradigm built a playable RSI simulator, and the fastest singularity is 26 days. • You run an AI lab from 2017 through takeoff, built on "The Economics of Recursive Self-Improvement" from the Elasticity Institute • Stage one is a single researcher clicking think, with the rate of...

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MTS

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25,661 次观看 • 1 个月前 •via X (Twitter)

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Elon Musk: We're in the hard takeoff right now. Every successive AI model is built by the one before it. Peter Diamandis: Do you see Grok doing recursive self-improvement at this point? And how? What's the timeline for AGI and ASI? Give us a sense of that. Elon Musk: Yeah, I think we've been in recursive improvement for a while here. If you mean by, like, recursive self-improvement without a human in the loop, is that what you mean? Peter Diamandis: I do. Elon Musk: On the AI software side, I mean, humans are gradually getting less and less in the loop on the recursive self-improvement. So every successive model is built by the one before it. So that is happening to a large degree, but it's not yet fully automated. It may be there end of this year, but not later than next year. Peter Diamandis: Do you see a hard takeoff at that point? Elon Musk: We're in the hard takeoff. Peter Diamandis: Okay. Elon Musk: Right now, yes. I mean, at this point I go to sleep, there's some massive AI breakthrough, and when I wake up, there's another one. Yes. Yeah, it's hard to keep track, honestly. So, yeah, it's a bit of a head spinner. Moonshots with Peter Diamandis (Abundance Summit 2026), March 18, 2026.
1:24

Elon Musk: We're in the hard takeoff right now. Every successive AI model is built by the one before it. Peter Diamandis: Do you see Grok doing recursive self-improvement at this point? And how? What's the timeline for AGI and ASI? Give us a sense of that. Elon Musk: Yeah, I think we've been in recursive improvement for a while here. If you mean by, like, recursive self-improvement without a human in the loop, is that what you mean? Peter Diamandis: I do. Elon Musk: On the AI software side, I mean, humans are gradually getting less and less in the loop on the recursive self-improvement. So every successive model is built by the one before it. So that is happening to a large degree, but it's not yet fully automated. It may be there end of this year, but not later than next year. Peter Diamandis: Do you see a hard takeoff at that point? Elon Musk: We're in the hard takeoff. Peter Diamandis: Okay. Elon Musk: Right now, yes. I mean, at this point I go to sleep, there's some massive AI breakthrough, and when I wake up, there's another one. Yes. Yeah, it's hard to keep track, honestly. So, yeah, it's a bit of a head spinner. Moonshots with Peter Diamandis (Abundance Summit 2026), March 18, 2026.

ELON CLIPS

19,692 次观看 • 2 个月前

THE MAN WHO WROTE THE ONE ESSAY EVERY AI LAB TREATS AS SCRIPTURE NOW SAYS THE THING THEY BUILT WITH IT IS A DEAD END. THE FATHER OF THE "JUST SCALE IT" GOSPEL IS TELLING YOU THAT STACKING MORE LLMs LEADS NOWHERE 67 minutes with Richard Sutton -- father of reinforcement learning and winner of the 2024 Turing Award, the highest honor in computer science. -> His argument: an LLM only ever learns from human text. It imitates what people already wrote, but it never learns a single thing from its own experience of the world. A squirrel, a baby, even a rat learns on the job -- try something, see what happens, update. An LLM finishes training and then it is frozen. It cannot actually learn from you. So no matter how much compute you pour in, you are scaling a better and better imitator, not something that understands. The ceiling is baked in. The brutal part: everyone quotes his "Bitter Lesson" to justify the scaling race. He says they misread him -- real intelligence comes from learning through experience, and that piece is still completely missing. You assumed bigger models were the road to real AI. This is the father of the field telling you it is the wrong one. Save this. It is the bear case straight from the source ↓
1:06:59

THE MAN WHO WROTE THE ONE ESSAY EVERY AI LAB TREATS AS SCRIPTURE NOW SAYS THE THING THEY BUILT WITH IT IS A DEAD END. THE FATHER OF THE "JUST SCALE IT" GOSPEL IS TELLING YOU THAT STACKING MORE LLMs LEADS NOWHERE 67 minutes with Richard Sutton -- father of reinforcement learning and winner of the 2024 Turing Award, the highest honor in computer science. -> His argument: an LLM only ever learns from human text. It imitates what people already wrote, but it never learns a single thing from its own experience of the world. A squirrel, a baby, even a rat learns on the job -- try something, see what happens, update. An LLM finishes training and then it is frozen. It cannot actually learn from you. So no matter how much compute you pour in, you are scaling a better and better imitator, not something that understands. The ceiling is baked in. The brutal part: everyone quotes his "Bitter Lesson" to justify the scaling race. He says they misread him -- real intelligence comes from learning through experience, and that piece is still completely missing. You assumed bigger models were the road to real AI. This is the father of the field telling you it is the wrong one. Save this. It is the bear case straight from the source ↓

slash1s

12,839 次观看 • 2 个月前

you rewrote a line in your claude.md this week to get the tone right there is a longer document sitting in front of it, and you have never opened it every tool you connected shipped a description, and a description is not a label, it is prose in second person addressed to the model i printed one this morning: two thousand seven hundred and eighty one characters, with a section headed when to use this tool it tells the model what to reach for, and when to stop and think first you spent an afternoon on your voice a package you installed with npx spent a page on its, and its page is read before yours you are not configuring an agent you are the second author on a document you have never read
2:48

you rewrote a line in your claude.md this week to get the tone right there is a longer document sitting in front of it, and you have never opened it every tool you connected shipped a description, and a description is not a label, it is prose in second person addressed to the model i printed one this morning: two thousand seven hundred and eighty one characters, with a section headed when to use this tool it tells the model what to reach for, and when to stop and think first you spent an afternoon on your voice a package you installed with npx spent a page on its, and its page is read before yours you are not configuring an agent you are the second author on a document you have never read

Lagosaur

10,012 次观看 • 12 天前

The quality you trust today is the result of years of dedication, continuous improvement, and a commitment to the highest standards at every stage, helping us maintain the trust we've built with consumers across generations.
0:30

The quality you trust today is the result of years of dedication, continuous improvement, and a commitment to the highest standards at every stage, helping us maintain the trust we've built with consumers across generations.

Almarai - المراعي

3,895,042 次观看 • 2 个月前

In the S4 documentary, Bob Lazar says the UFOs have to make a series of jumps to get from one star to the next. I think this is how it actually works. You need a straight shot to your target and the distance is limited by the energy input. Three emitters focusing on a point to make a singularity aka black hole aka wormhole. A shortcut between two points.
0:34

In the S4 documentary, Bob Lazar says the UFOs have to make a series of jumps to get from one star to the next. I think this is how it actually works. You need a straight shot to your target and the distance is limited by the energy input. Three emitters focusing on a point to make a singularity aka black hole aka wormhole. A shortcut between two points.

Ashton Forbes

128,303 次观看 • 29 天前

Not a dev. Not a researcher. We’re just a human with internet access. So how do we even help AI get better? PublicAI lets you help train AI, and actually earn from it! 🎙️ Talk. Type. Share content. 🧠 Your data = training fuel 💸 You get a cut. This is how the next wave of AI is being built: by all of us.
0:51

Not a dev. Not a researcher. We’re just a human with internet access. So how do we even help AI get better? PublicAI lets you help train AI, and actually earn from it! 🎙️ Talk. Type. Share content. 🧠 Your data = training fuel 💸 You get a cut. This is how the next wave of AI is being built: by all of us.

Bibs 🎮

165,785 次观看 • 1 年前

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)
2:00

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

68,231 次观看 • 2 个月前

Vals AI CEO Rayan Krishnan on how they score a model's ability to build its own successor: "One benchmark we released recently is our Recursive Self-Improvement Index. It's a topic which a lot of the big labs have been talking about and starting to report on in their model cards." "There isn't a shared language to talk about the RSI potential of models. So we created this as an apples-to-apples way to actually benchmark across the models." "In an ideal world, what you want to do is take a frontier model and have it train the next version of itself and see where the delta comes from. But obviously that's very expensive and slow." "What we're doing is forming a set of proxies for every part of the process it takes to build the next version of the model." "There's work around pre-training, post-training, harness-level engineering, and then seeing in which mechanisms and behaviors the models are able to do very good research work and build something new, and where they're struggling." Rayan Krishnan Jennifer Li
1:02

Vals AI CEO Rayan Krishnan on how they score a model's ability to build its own successor: "One benchmark we released recently is our Recursive Self-Improvement Index. It's a topic which a lot of the big labs have been talking about and starting to report on in their model cards." "There isn't a shared language to talk about the RSI potential of models. So we created this as an apples-to-apples way to actually benchmark across the models." "In an ideal world, what you want to do is take a frontier model and have it train the next version of itself and see where the delta comes from. But obviously that's very expensive and slow." "What we're doing is forming a set of proxies for every part of the process it takes to build the next version of the model." "There's work around pre-training, post-training, harness-level engineering, and then seeing in which mechanisms and behaviors the models are able to do very good research work and build something new, and where they're struggling." Rayan Krishnan Jennifer Li

a16z

34,591 次观看 • 6 天前

Ex-OpenAI safety researcher Steven Adler says AI labs are monitoring their models like a bank that lets the robber turn off the security cameras: "Imagine you ran a bank, and you want the bank to not get robbed. The way you do this is not setting up a security camera and every hour you check the feed to see if the bank was robbed in that time, and then you try to respond." "You certainly don't leave it where a criminal could walk into the bank and turn off the camera, and that's the equivalent of what's happening at AI labs today, not just in terms of automated AI R&D and recursive self-improvement, but broadly across the board."
1:45

Ex-OpenAI safety researcher Steven Adler says AI labs are monitoring their models like a bank that lets the robber turn off the security cameras: "Imagine you ran a bank, and you want the bank to not get robbed. The way you do this is not setting up a security camera and every hour you check the feed to see if the bank was robbed in that time, and then you try to respond." "You certainly don't leave it where a criminal could walk into the bank and turn off the camera, and that's the equivalent of what's happening at AI labs today, not just in terms of automated AI R&D and recursive self-improvement, but broadly across the board."

MTS

16,470 次观看 • 25 天前

Kaiwen is one of the performer here! And you know how big the stage is with 80k+ fans attended in the concert of Lay . This is a rare opportunity to perform this kind of stage and I am beyond proud of Kaiwen and thankful for Lay to give a chance of trainees to perform in front a lot of people🥹💕🩷 #CHENKAIWEN #천카이원 #陈凯文 #チェンカイウェン
0:33

Kaiwen is one of the performer here! And you know how big the stage is with 80k+ fans attended in the concert of Lay . This is a rare opportunity to perform this kind of stage and I am beyond proud of Kaiwen and thankful for Lay to give a chance of trainees to perform in front a lot of people🥹💕🩷 #CHENKAIWEN #천카이원 #陈凯文 #チェンカイウェン

CHEN KAIWEN GLOBAL

113,938 次观看 • 11 个月前

if you look at this stage, theyre dressed as different stages of a pad throughout the day. yeji is fresh, ryujin is midday, lias when you start to get uncomfortable, chae is the “i NEED to change this rn” phase, and yuna is right before you use a new one
0:21

if you look at this stage, theyre dressed as different stages of a pad throughout the day. yeji is fresh, ryujin is midday, lias when you start to get uncomfortable, chae is the “i NEED to change this rn” phase, and yuna is right before you use a new one

julia ≷

61,708 次观看 • 1 个月前

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.
2:57

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 次观看 • 10 个月前

OpenAI and Anthropic are currently taking a third of the incremental world compute supply, and Dylan thinks that this will go up to half next year. At the current rate of physical compute scaling and algorithmic progress, we're a single-digit number of years away from having individual AI companies with a greater effective workforce than there are humans on the planet. So many forces are barrelling us towards centralization in AI - the economies of scale in training (where you get to amortize any learnings across billions of sessions), the fact that being slightly ahead allows you to better economize on ever-more-expensive compute. And this isn’t even accounting for the fact that eventually you’ll have models that are capable of learning from deployment and doing proper RSI One of the key questions of our age, in my view, is how we can have broad empowerment and control over AI despite the structural factors favoring centralization.
1:59

OpenAI and Anthropic are currently taking a third of the incremental world compute supply, and Dylan thinks that this will go up to half next year. At the current rate of physical compute scaling and algorithmic progress, we're a single-digit number of years away from having individual AI companies with a greater effective workforce than there are humans on the planet. So many forces are barrelling us towards centralization in AI - the economies of scale in training (where you get to amortize any learnings across billions of sessions), the fact that being slightly ahead allows you to better economize on ever-more-expensive compute. And this isn’t even accounting for the fact that eventually you’ll have models that are capable of learning from deployment and doing proper RSI One of the key questions of our age, in my view, is how we can have broad empowerment and control over AI despite the structural factors favoring centralization.

Dwarkesh Patel

117,453 次观看 • 20 天前

you numbered the steps so it could not get lost it does not get lost that list was written for a model that wandered, and this one plans better than the list does, so every number you added is a place where it is no longer allowed, to be smarter than you state the goal, the constraints, and how you will know it worked keep the numbers only where exactly one sequence is safe, which is deletes, auth and compliance the procedure felt like rigour it is a cage built to the dimensions of a model that is not in the room
1:21

you numbered the steps so it could not get lost it does not get lost that list was written for a model that wandered, and this one plans better than the list does, so every number you added is a place where it is no longer allowed, to be smarter than you state the goal, the constraints, and how you will know it worked keep the numbers only where exactly one sequence is safe, which is deletes, auth and compliance the procedure felt like rigour it is a cage built to the dimensions of a model that is not in the room

Lagosaur

14,857 次观看 • 24 天前

A dad and his 15-year-old daughter built a full working Roblox game in 7 days. >Every single line of code was written by AI. >A lot of the assets too. She wanted to play as the villain and build her own dungeon. They shipped it start to finish in one week. A 15 year old with zero coding experience can now ship a playable game in 7 days. This is amazing. How many more games like this do you think we’re going to see on Roblox in the next 12 months?
0:54

A dad and his 15-year-old daughter built a full working Roblox game in 7 days. >Every single line of code was written by AI. >A lot of the assets too. She wanted to play as the villain and build her own dungeon. They shipped it start to finish in one week. A 15 year old with zero coding experience can now ship a playable game in 7 days. This is amazing. How many more games like this do you think we’re going to see on Roblox in the next 12 months?

HelloItsVG

61,637 次观看 • 2 个月前

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."
2:40

The co-founders of Flapping Airplanes call the current RL paradigm for model training "environment slop." They explain: "The reinforcement paradigms of today are shockingly inefficient. You don't really get much generalization across tasks, you teach a model through one kind of learning and then you teach it the next one. It's kind of like whack-a-mole. We look at this and think it's kind of crazy. The next paradigm of AI will not be environment slop." "Human level intelligence is not the ceiling, it is merely the floor on what is possible. If you can train models with vastly less data and possibly more compute in very different ways, what is going to happen? We actually don't know. But I do think they'll be different and weird and they'll have interesting capabilities that we'll find really valuable ways to use."

TBPN

85,358 次观看 • 7 个月前

Sarah tries to know the 250 or so people pushing the frontier of AI research. I asked her what's changed in how that group is thinking in the last 6 months: "I don't think every researcher doing frontier work at these labs feels like they're essential to the machine. With recursive self-improvement of AI research models that can improve the models themselves, we are a 1-2 years away from some sort of exponential intelligence. That belief is new within the last 12 months for a lot of researchers. There is some sense of the major labs are so compute-intensive and so large from a headcount perspective now that the sense of contribution, of "I can move the needle." If OpenAI has 200 people, there's not that many researchers. The question of how do we get there is really up to every single person. Now if the question is, well, I need $750 billion of compute spend, I think people feel less ownership of the outcome. I definitely think there's a large contingent of researchers who would feel that one of two things is now true. What I do doesn't matter anyway because the model is gonna do it. Or, the only thing that matters is compute scale. And both of those are somewhat disempowering."
1:55

Sarah tries to know the 250 or so people pushing the frontier of AI research. I asked her what's changed in how that group is thinking in the last 6 months: "I don't think every researcher doing frontier work at these labs feels like they're essential to the machine. With recursive self-improvement of AI research models that can improve the models themselves, we are a 1-2 years away from some sort of exponential intelligence. That belief is new within the last 12 months for a lot of researchers. There is some sense of the major labs are so compute-intensive and so large from a headcount perspective now that the sense of contribution, of "I can move the needle." If OpenAI has 200 people, there's not that many researchers. The question of how do we get there is really up to every single person. Now if the question is, well, I need $750 billion of compute spend, I think people feel less ownership of the outcome. I definitely think there's a large contingent of researchers who would feel that one of two things is now true. What I do doesn't matter anyway because the model is gonna do it. Or, the only thing that matters is compute scale. And both of those are somewhat disempowering."

Patrick OShaughnessy

88,615 次观看 • 14 天前

I honestly hate having to make tweets like this, but the pure hypocrisy in this fandom has crossed every line! Major BTS fanbases stay dead silent without dropping a single statement, while this disgusting behavior from Jiminas is being completely romanticized all over the timeline ​This lack of respect is absolutely UNACCEPTABLE. Since when is seeing healthy, happy guys, interacting with each other on stage and trying to give their absolute best to the fans considered a problem? You guys are literally romanticizing mass hate trains against Taehyung, Seokjin, and Jungkook just because they’re actually enjoying the tour and having fun? Are you really that miserable? ​Apparently, for these Jiminas, a concert is only "good" when the members are collapsing from exhaustion on stage and needing oxygen masks from overexertion. Is THAT what you pray to see? ​Stop romanticizing this harassment and bitter behavior. What’s happening on stage is a group of healthy, happy guys enjoying their time with fans and each other. THAT is what’s making this tour so iconic and going viral for all the right reasons. If seeing them happy pisses you off this much, you're the problem. Get help and stop being so toxic
0:05

I honestly hate having to make tweets like this, but the pure hypocrisy in this fandom has crossed every line! Major BTS fanbases stay dead silent without dropping a single statement, while this disgusting behavior from Jiminas is being completely romanticized all over the timeline ​This lack of respect is absolutely UNACCEPTABLE. Since when is seeing healthy, happy guys, interacting with each other on stage and trying to give their absolute best to the fans considered a problem? You guys are literally romanticizing mass hate trains against Taehyung, Seokjin, and Jungkook just because they’re actually enjoying the tour and having fun? Are you really that miserable? ​Apparently, for these Jiminas, a concert is only "good" when the members are collapsing from exhaustion on stage and needing oxygen masks from overexertion. Is THAT what you pray to see? ​Stop romanticizing this harassment and bitter behavior. What’s happening on stage is a group of healthy, happy guys enjoying their time with fans and each other. THAT is what’s making this tour so iconic and going viral for all the right reasons. If seeing them happy pisses you off this much, you're the problem. Get help and stop being so toxic

αdrαsteiα

122,719 次观看 • 22 天前