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Been thinking about why most AI research tools fail for serious ML work. The answer is simpler than people admit. ChatGPT optimize for plausibility. That works for drafts, summaries, brainstorming. It breaks the moment you need something verifiable— when the question isn’t just “what’s the answer,” but “is this...

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Poonam Soni

215,734 subscribers

27,512 views • 4 months ago •via X (Twitter)

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I almost published something wrong last week. Not a typo. A claim I'd accepted too fast. A source I hadn't traced all the way back. Caught it at step 247 of 300. That's what MiroMind's auditable reasoning chain actually looks like in practice not a feature you read about in a changelog. a moment where you see it work. I'd typed in something simple: "latest reasoning model benchmarks" Straightforward prompt. The kind you'd throw at ChatGPT without thinking twice. What came back wasn't a confident summary. It was a structured map where the benchmarks agree, where the eval setups are cherry-picked, and where the leaderboard story gets complicated in production. Step 247: a source I hadn't verified. An inference that looked right but wasn't traceable. Flagged. Not included. Most AI tools give you an answer. This one shows you how it got there. Every step. Every source. Replayable. If a step can't be verified it doesn't make it into the output. That's not a safety feature. That's a completely different architecture. Has AI ever confidently given you something that turned out to be wrong? → Worth trying if you’re doing anything beyond surface-level research. $19/mo or pay-per-use
1:24

I almost published something wrong last week. Not a typo. A claim I'd accepted too fast. A source I hadn't traced all the way back. Caught it at step 247 of 300. That's what MiroMind's auditable reasoning chain actually looks like in practice not a feature you read about in a changelog. a moment where you see it work. I'd typed in something simple: "latest reasoning model benchmarks" Straightforward prompt. The kind you'd throw at ChatGPT without thinking twice. What came back wasn't a confident summary. It was a structured map where the benchmarks agree, where the eval setups are cherry-picked, and where the leaderboard story gets complicated in production. Step 247: a source I hadn't verified. An inference that looked right but wasn't traceable. Flagged. Not included. Most AI tools give you an answer. This one shows you how it got there. Every step. Every source. Replayable. If a step can't be verified it doesn't make it into the output. That's not a safety feature. That's a completely different architecture. Has AI ever confidently given you something that turned out to be wrong? → Worth trying if you’re doing anything beyond surface-level research. $19/mo or pay-per-use

Shruti

28,869 views • 4 months ago

Tim Dillon just said what everyone is thinking. Trump is in “lockstep” with the deep state. “He’s in Iran.” “He kidnapped Maduro.” “Flock cameras are everywhere.” “Surveillance technology’s everywhere.” “The tech people are getting all the money.” “The pace at which unregulated AI is going to grow and become a real problem is harrowing.” “Why isn’t he investigating his own assassination attempt?” “If you were just gonna blame everything on the deep state, then what the hell was the point?” “I’m not saying there’s not some permanent government apparatus.” “I’ve talked about it a million times.” “But for the people that ran on upending the deep state to then come in and blame things on the deep state, it seems like you have to be an idiot for that to work.” “Why did anyone think it would be interesting for this guy to win if he was just going to do all the things that this deep state wants him to do?” “What is he doing that they don’t like?” “Who are these people that are really upset and up in arms with him?” “He seems to be pretty lockstep with all of these people.” Tim Dillon
2:45

Tim Dillon just said what everyone is thinking. Trump is in “lockstep” with the deep state. “He’s in Iran.” “He kidnapped Maduro.” “Flock cameras are everywhere.” “Surveillance technology’s everywhere.” “The tech people are getting all the money.” “The pace at which unregulated AI is going to grow and become a real problem is harrowing.” “Why isn’t he investigating his own assassination attempt?” “If you were just gonna blame everything on the deep state, then what the hell was the point?” “I’m not saying there’s not some permanent government apparatus.” “I’ve talked about it a million times.” “But for the people that ran on upending the deep state to then come in and blame things on the deep state, it seems like you have to be an idiot for that to work.” “Why did anyone think it would be interesting for this guy to win if he was just going to do all the things that this deep state wants him to do?” “What is he doing that they don’t like?” “Who are these people that are really upset and up in arms with him?” “He seems to be pretty lockstep with all of these people.” Tim Dillon

Holden Culotta

280,974 views • 20 days ago

They call you racist, and think the conversation is over. News flash, this is not 1964 anymore. The American people are a lot smarter than that, and they’re not buying it. Because the reality is, people like me exist. What does that tell you? It tells you this country has made real progress. It tells you that millions of Americans are doing exactly what Martin Luther King Jr. called for, judging people not by the color of their skin, but by the content of their character. That’s not theory, that’s reality. But instead of celebrating that progress, some want to drag us backwards, into a system where race is everything, where success is assigned, where outcomes are engineered. That’s not empowerment, that’s limitation. The idea that someone can only succeed if a district is drawn for them, or if a box is checked for them, is an insult to the very people it claims to help. Here’s a better idea, be a great candidate. Work hard. Earn trust. Serve people. Put your name on the ballot and let voters decide.
1:27

They call you racist, and think the conversation is over. News flash, this is not 1964 anymore. The American people are a lot smarter than that, and they’re not buying it. Because the reality is, people like me exist. What does that tell you? It tells you this country has made real progress. It tells you that millions of Americans are doing exactly what Martin Luther King Jr. called for, judging people not by the color of their skin, but by the content of their character. That’s not theory, that’s reality. But instead of celebrating that progress, some want to drag us backwards, into a system where race is everything, where success is assigned, where outcomes are engineered. That’s not empowerment, that’s limitation. The idea that someone can only succeed if a district is drawn for them, or if a box is checked for them, is an insult to the very people it claims to help. Here’s a better idea, be a great candidate. Work hard. Earn trust. Serve people. Put your name on the ballot and let voters decide.

Wesley Hunt

267,496 views • 3 months ago

#WATCH | Kuala Lumpur, Malaysia: On his tweet, member of all-party delegation led by JD(U) MP Sanjay Kumar Jha, Congress leader Salman Khurshid says, "People keep saying, 'what are you doing in a deleagtion where there are people from the BJP. What are you doing there?' What are we doing here? We are doing here is, what is needed for the country. No matter which party you are from, today what is needed is a single voice to speak in favour of the nation and that is what we are doing here. I think when I say is it so difficult to be a patriot? - that is the question that has to be asked of those who are putting out tweets and saying things that I think I believe are not very encouraging when you want to do something for the nation."
0:49

#WATCH | Kuala Lumpur, Malaysia: On his tweet, member of all-party delegation led by JD(U) MP Sanjay Kumar Jha, Congress leader Salman Khurshid says, "People keep saying, 'what are you doing in a deleagtion where there are people from the BJP. What are you doing there?' What are we doing here? We are doing here is, what is needed for the country. No matter which party you are from, today what is needed is a single voice to speak in favour of the nation and that is what we are doing here. I think when I say is it so difficult to be a patriot? - that is the question that has to be asked of those who are putting out tweets and saying things that I think I believe are not very encouraging when you want to do something for the nation."

ANI

24,651 views • 1 year ago

I stopped trusting AI outputs blindly. Not because AI is bad… But because I started noticing a pattern. The more I used AI for serious work, the more I had to: → double-check everything → re-verify sources → question the logic behind answers At some point, I thought: “What’s the point of saving time… if I still don’t trust the output?” That’s when I came across MiroMindAI and it felt different from day one. Not flashy. Not trying to impress. Just… built for accuracy. I tested it the same way I test any tool: Real use cases. No hype. • Deep research • Multi-source validation • Complex reasoning tasks And here’s what stood out 👇 🧠 It shows how it thinks Not just answers actual reasoning chains you can read, audit, and replay. 🔍 It doesn’t “summarize”… it investigates Pulls from hundreds of sources and builds structured, evidence-backed reports. ⚖️ It verifies itself before responding Multiple layers checking the output (something most AI tools skip completely) And honestly, this is what clicked for me: Most AI tools today are like 👉 smart interns (fast, helpful, but need supervision) MiroMind feels more like 👉 a senior analyst (slower, but you can rely on it) 💡 One simple shift I noticed: Before: 10 tabs open → cross-checking → still unsure Now: 1 report → clear reasoning → backed by sources I’m not saying this replaces expertise. But it does reduce the noise. A lot. If you’re someone who works in: • research • finance • legal • healthcare You’ll probably appreciate this more than others. 👉
0:38

I stopped trusting AI outputs blindly. Not because AI is bad… But because I started noticing a pattern. The more I used AI for serious work, the more I had to: → double-check everything → re-verify sources → question the logic behind answers At some point, I thought: “What’s the point of saving time… if I still don’t trust the output?” That’s when I came across MiroMindAI and it felt different from day one. Not flashy. Not trying to impress. Just… built for accuracy. I tested it the same way I test any tool: Real use cases. No hype. • Deep research • Multi-source validation • Complex reasoning tasks And here’s what stood out 👇 🧠 It shows how it thinks Not just answers actual reasoning chains you can read, audit, and replay. 🔍 It doesn’t “summarize”… it investigates Pulls from hundreds of sources and builds structured, evidence-backed reports. ⚖️ It verifies itself before responding Multiple layers checking the output (something most AI tools skip completely) And honestly, this is what clicked for me: Most AI tools today are like 👉 smart interns (fast, helpful, but need supervision) MiroMind feels more like 👉 a senior analyst (slower, but you can rely on it) 💡 One simple shift I noticed: Before: 10 tabs open → cross-checking → still unsure Now: 1 report → clear reasoning → backed by sources I’m not saying this replaces expertise. But it does reduce the noise. A lot. If you’re someone who works in: • research • finance • legal • healthcare You’ll probably appreciate this more than others. 👉

Md Riyazuddin

20,990 views • 4 months ago

Are you ready to be drained today? se, Co-Founder of fomo, stated that all of Fomo's code was built by AI. Even the non-tech team writes code using AI. For security purposes, all of this code is audited by humans, but don't you think that after a $94M fundraise, the platform should be completely safe for users? Because of vibecode, many people are raising questions. Se Young also stated that the team still hasn't used the money from the latest $75M raise and is growing on revenue. For me, it is completely unclear why a small team that has stable revenue and runs everything on vibecode needs that much money. You don't want to scale the team, and you've had affiliates for a long time. Where do you plan to spend this $75M, and is the platform safe right now? Sw, in the podcast you said you answer all questions on CT and this question is currently the most important one. Funds are safu?
1:06

Are you ready to be drained today? se, Co-Founder of fomo, stated that all of Fomo's code was built by AI. Even the non-tech team writes code using AI. For security purposes, all of this code is audited by humans, but don't you think that after a $94M fundraise, the platform should be completely safe for users? Because of vibecode, many people are raising questions. Se Young also stated that the team still hasn't used the money from the latest $75M raise and is growing on revenue. For me, it is completely unclear why a small team that has stable revenue and runs everything on vibecode needs that much money. You don't want to scale the team, and you've had affiliates for a long time. Where do you plan to spend this $75M, and is the platform safe right now? Sw, in the podcast you said you answer all questions on CT and this question is currently the most important one. Funds are safu?

ProMint

99,654 views • 28 days ago

Anthropic CEO: "It is absolutely wild that people are talking about the same tired political issues, when we are near the end of the AI exponential." Dario Amodei spent 10 minutes explaining why almost nobody sees it. here's what he covers: > in 2017 he predicted exactly where AI would be today. he was right. > models went from "smart high school student" to "PhD-level work" step by step as planned. > the progress has not slowed down, not even close. > none of this surprised him, what surprised him is that nobody else sees it. the world is still focused on everything else. new episodes, memes, celebrity beef, election news.. meanwhile the most important thing happening right now goes unnoticed. this is not a slow shift, it never was. the people who understand this early and actually start using these tools properly are going to be in a completely different position than everyone else, that gap is already opening. the guide on how to be on the right side of it is in the article below.
10:39

Anthropic CEO: "It is absolutely wild that people are talking about the same tired political issues, when we are near the end of the AI exponential." Dario Amodei spent 10 minutes explaining why almost nobody sees it. here's what he covers: > in 2017 he predicted exactly where AI would be today. he was right. > models went from "smart high school student" to "PhD-level work" step by step as planned. > the progress has not slowed down, not even close. > none of this surprised him, what surprised him is that nobody else sees it. the world is still focused on everything else. new episodes, memes, celebrity beef, election news.. meanwhile the most important thing happening right now goes unnoticed. this is not a slow shift, it never was. the people who understand this early and actually start using these tools properly are going to be in a completely different position than everyone else, that gap is already opening. the guide on how to be on the right side of it is in the article below.

Anatoli Kopadze

1,144,510 views • 3 months ago

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below
0:50

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below

Hanako

13,683 views • 9 days ago

For the first time ever, you can buy ads inside ChatGPT. And because the platform is still new, almost nobody has figured out how to use it properly yet. That means the opportunity is wide open. Here’s what changed. OpenAI launched a self-serve Ads Manager, making ChatGPT ads accessible to businesses that previously would not have been able to participate. You no longer need to be a massive brand with an enormous advertising budget. But these ads work differently from Google. Google usually matches an ad to the words someone searches. ChatGPT looks at the larger context of the conversation and what that person is trying to accomplish. That creates a new challenge: how do you know which conversations your business should appear in? You start with real demand. What are people already searching for in your industry? What questions are they asking? What problems are they trying to solve? And what language do they use to describe those problems? That’s where Ubersuggest comes in. Use it to find the topics people search for most, see how competitive they are and identify where your competitors are already getting attention. Then turn those insights into the conversation themes, ad angles and landing pages you use for your ChatGPT campaigns. The technology is new, but the principle is not: Understand what people want, learn how they talk about it and show up when they are ready to make a decision. The companies that learn this early will have an advantage while everyone else is still trying to understand how it works.
1:12

For the first time ever, you can buy ads inside ChatGPT. And because the platform is still new, almost nobody has figured out how to use it properly yet. That means the opportunity is wide open. Here’s what changed. OpenAI launched a self-serve Ads Manager, making ChatGPT ads accessible to businesses that previously would not have been able to participate. You no longer need to be a massive brand with an enormous advertising budget. But these ads work differently from Google. Google usually matches an ad to the words someone searches. ChatGPT looks at the larger context of the conversation and what that person is trying to accomplish. That creates a new challenge: how do you know which conversations your business should appear in? You start with real demand. What are people already searching for in your industry? What questions are they asking? What problems are they trying to solve? And what language do they use to describe those problems? That’s where Ubersuggest comes in. Use it to find the topics people search for most, see how competitive they are and identify where your competitors are already getting attention. Then turn those insights into the conversation themes, ad angles and landing pages you use for your ChatGPT campaigns. The technology is new, but the principle is not: Understand what people want, learn how they talk about it and show up when they are ready to make a decision. The companies that learn this early will have an advantage while everyone else is still trying to understand how it works.

Neil Patel

12,249 views • 7 days ago

Raleigh-Durham might be the most underrated food city on the East Coast right now. And if you’ve been paying attention, you know that’s saying something. This is where serious chefs are doing thoughtful, ingredient-driven work without the noise. Where global flavors and Southern roots actually mean something on the plate. Where you can eat incredibly well without fighting for a reservation three weeks out. Don’t wait for the headlines to catch up. Get there now. What do you think is the most underrated food city on the East Coast?
0:57

Raleigh-Durham might be the most underrated food city on the East Coast right now. And if you’ve been paying attention, you know that’s saying something. This is where serious chefs are doing thoughtful, ingredient-driven work without the noise. Where global flavors and Southern roots actually mean something on the plate. Where you can eat incredibly well without fighting for a reservation three weeks out. Don’t wait for the headlines to catch up. Get there now. What do you think is the most underrated food city on the East Coast?

Andrew Zimmern

264,405 views • 4 months ago

AI Overviews are showing up for more and more searches. And when they do, fewer people click. That’s the part everyone is panicking about. Google gives the answer right there, so the user doesn’t always need to visit your site. But SEO is not dead. The game just changed. Because now your job is not only to rank on Google. Your job is to become the source AI trusts enough to recommend. That means showing up in Google AI Overviews, ChatGPT, Perplexity, Gemini, and all the other places people are asking questions. This is why tools like Ubersuggest matter even more now. You can use it to see what people are searching for, what questions they’re asking, what competitors rank for, and where your content gaps are. Then you turn that into better answers. Not generic blog posts. Real expertise. Real examples. Clear explanations. Content that both humans and AI can understand, trust, and cite. And your website still matters because that’s where attention turns into action. AI can summarize information. But it does not close the deal for you. It does not build the relationship. It does not replace your proof, your offer, or your brand. So save this action plan.
1:03

AI Overviews are showing up for more and more searches. And when they do, fewer people click. That’s the part everyone is panicking about. Google gives the answer right there, so the user doesn’t always need to visit your site. But SEO is not dead. The game just changed. Because now your job is not only to rank on Google. Your job is to become the source AI trusts enough to recommend. That means showing up in Google AI Overviews, ChatGPT, Perplexity, Gemini, and all the other places people are asking questions. This is why tools like Ubersuggest matter even more now. You can use it to see what people are searching for, what questions they’re asking, what competitors rank for, and where your content gaps are. Then you turn that into better answers. Not generic blog posts. Real expertise. Real examples. Clear explanations. Content that both humans and AI can understand, trust, and cite. And your website still matters because that’s where attention turns into action. AI can summarize information. But it does not close the deal for you. It does not build the relationship. It does not replace your proof, your offer, or your brand. So save this action plan.

Neil Patel

10,183 views • 25 days ago

Blogging used to be simple. Write something helpful, rank on Google, and wake up to traffic. That worked for a long time. Then AI got good at summarizing it. A recent study tracked 100 once-successful blogs. The median one lost 85% of its Google traffic. But the split is what matters. The blogs getting destroyed right now are mostly describing things “best budgeting apps,” “how to start a podcast,” “top tools in 2026.” The ones still growing? They’re proving something. They didn’t write another roundup. They built the thing, tested it for real, spent the money, made the mistake, and showed what actually happened. That’s the new filter. Before you create your next piece of content, look at what people are searching for using then you can create content for what people actually want to know about. Then ask yourself one question: Can AI answer this without me? If the answer is yes, you’re writing something a machine can already summarize better and faster than you. If the answer is no, if it requires your experience, your specific results, your data, or your actual outcome that’s where the opportunity is. The future of content isn’t more articles. It’s proof that can’t be faked.
1:07

Blogging used to be simple. Write something helpful, rank on Google, and wake up to traffic. That worked for a long time. Then AI got good at summarizing it. A recent study tracked 100 once-successful blogs. The median one lost 85% of its Google traffic. But the split is what matters. The blogs getting destroyed right now are mostly describing things “best budgeting apps,” “how to start a podcast,” “top tools in 2026.” The ones still growing? They’re proving something. They didn’t write another roundup. They built the thing, tested it for real, spent the money, made the mistake, and showed what actually happened. That’s the new filter. Before you create your next piece of content, look at what people are searching for using then you can create content for what people actually want to know about. Then ask yourself one question: Can AI answer this without me? If the answer is yes, you’re writing something a machine can already summarize better and faster than you. If the answer is no, if it requires your experience, your specific results, your data, or your actual outcome that’s where the opportunity is. The future of content isn’t more articles. It’s proof that can’t be faked.

Neil Patel

11,671 views • 27 days ago

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."
6:17

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Molly O’Shea

12,816 views • 1 month ago

Spencer Pratt is asked what he can say to people that live with the filth in LA, but still want to vote for the same stuff. His answer: "I'm not actually trying to convert these people because they're clearly living in a different dimension. The people I'm getting to vote for me, their eyeballs work." "Even my older sister, who has voted for every single Democrat since she was 18, she's voting for me. All of her friends, anyone that doesn't want to drive in poop, step in poop, step on a fentanyl needle, have their kids see naked drug addicts on the sidewalk. These are all the people that are voting for me." "So the people that would vote for Nithya Raman, who's actually been in charge of the homelessness and housing for the city council for the last three years, who works hand in hand with Mayor Bass…" "If you want more human poop, drug addicts, no lights, potholes, everything that's failing, these are the people who are failing." Elect this man, Los Angeles.
1:17

Spencer Pratt is asked what he can say to people that live with the filth in LA, but still want to vote for the same stuff. His answer: "I'm not actually trying to convert these people because they're clearly living in a different dimension. The people I'm getting to vote for me, their eyeballs work." "Even my older sister, who has voted for every single Democrat since she was 18, she's voting for me. All of her friends, anyone that doesn't want to drive in poop, step in poop, step on a fentanyl needle, have their kids see naked drug addicts on the sidewalk. These are all the people that are voting for me." "So the people that would vote for Nithya Raman, who's actually been in charge of the homelessness and housing for the city council for the last three years, who works hand in hand with Mayor Bass…" "If you want more human poop, drug addicts, no lights, potholes, everything that's failing, these are the people who are failing." Elect this man, Los Angeles.

Western Lensman

860,795 views • 2 months ago

This clip makes it abundantly clear the The Labour Party government is planning to introduce a copyright exception for commercial research (read: AI training) - which would be disastrous for British creatives. When asked the simple question of whether AI companies will be required to license copyrighted work they want to train on in the UK (as they are required to do at the moment!), this government minister says: "Our priority is to ensure that the UK is ready for AI-related risks while supporting responsible innovation and long-term growth." If you are planning on maintaining robust copyright laws that protect creators, this is not the answer you give. This is the answer you give if you're preparing to try to justify upending the law and throwing creatives under the bus. Creatives in the UK gearing up for a big fight, I think.
0:39

This clip makes it abundantly clear the The Labour Party government is planning to introduce a copyright exception for commercial research (read: AI training) - which would be disastrous for British creatives. When asked the simple question of whether AI companies will be required to license copyrighted work they want to train on in the UK (as they are required to do at the moment!), this government minister says: "Our priority is to ensure that the UK is ready for AI-related risks while supporting responsible innovation and long-term growth." If you are planning on maintaining robust copyright laws that protect creators, this is not the answer you give. This is the answer you give if you're preparing to try to justify upending the law and throwing creatives under the bus. Creatives in the UK gearing up for a big fight, I think.

Ed Newton-Rex

17,168 views • 5 months ago

Elon Musk just said the quiet part out loud about government and AI. Musk: “AI is moving 10 times faster than government, maybe more.” Not slightly ahead. Not a few years out in front. Ten times faster. And pulling away. Every regulatory body on earth runs on the same architecture. Committees form. Hearings are scheduled. Legislation is drafted, debated, revised, and passed. By the time a law exists, the thing it was written to govern has already moved three generations beyond it. That architecture was built for a world that moves at human speed. This world does not. Musk: “The one thing that the government can do is just issue people money.” Not regulate. Not protect. Not steer. Issue money. That is not a policy position. That is a surrender. The most powerful governments on earth, sitting on top of the most sophisticated legal and military infrastructure in human history, reduced to a single remaining function. Sending people checks. Because they cannot move fast enough to do anything else. Now sit with what that actually means. For ten thousand years, the central bargain of civilization was simple. You contribute labor. Society functions. You eat. The system needed you. That bargain is being quietly retired. The machine does not need you to run the factory. Does not need you to process the paperwork. Does not need you to write the code or drive the truck or staff the call center. And the government already knows it. Musk: “Nobody’s gonna starve is what I’m saying.” He is right. The floor is rising. Survival is becoming guaranteed. That should feel like the finish line. For most of human history, it would have been. But here is what nobody is saying out loud. The hard part was never survival. The hard part is what happens to a species that spent ten millennia being defined by its need to survive, the moment that need disappears. Purpose is not something the government can deposit into your account. A check covers rent. It does not answer the question of what you are for. When the thing that organized your days, justified your effort, and gave your life a legible shape gets handed to a machine, you do not automatically inherit freedom. You inherit a void. And a void with a guaranteed income is still a void. The people who will matter in this era are not the ones who cash the check and wait. They are the ones who hear the starting gun in it. For the first time in history, the baseline is solved. Which means the only question left is the one every generation before yours was too buried to ask. What are you actually here to build.
0:46

Elon Musk just said the quiet part out loud about government and AI. Musk: “AI is moving 10 times faster than government, maybe more.” Not slightly ahead. Not a few years out in front. Ten times faster. And pulling away. Every regulatory body on earth runs on the same architecture. Committees form. Hearings are scheduled. Legislation is drafted, debated, revised, and passed. By the time a law exists, the thing it was written to govern has already moved three generations beyond it. That architecture was built for a world that moves at human speed. This world does not. Musk: “The one thing that the government can do is just issue people money.” Not regulate. Not protect. Not steer. Issue money. That is not a policy position. That is a surrender. The most powerful governments on earth, sitting on top of the most sophisticated legal and military infrastructure in human history, reduced to a single remaining function. Sending people checks. Because they cannot move fast enough to do anything else. Now sit with what that actually means. For ten thousand years, the central bargain of civilization was simple. You contribute labor. Society functions. You eat. The system needed you. That bargain is being quietly retired. The machine does not need you to run the factory. Does not need you to process the paperwork. Does not need you to write the code or drive the truck or staff the call center. And the government already knows it. Musk: “Nobody’s gonna starve is what I’m saying.” He is right. The floor is rising. Survival is becoming guaranteed. That should feel like the finish line. For most of human history, it would have been. But here is what nobody is saying out loud. The hard part was never survival. The hard part is what happens to a species that spent ten millennia being defined by its need to survive, the moment that need disappears. Purpose is not something the government can deposit into your account. A check covers rent. It does not answer the question of what you are for. When the thing that organized your days, justified your effort, and gave your life a legible shape gets handed to a machine, you do not automatically inherit freedom. You inherit a void. And a void with a guaranteed income is still a void. The people who will matter in this era are not the ones who cash the check and wait. They are the ones who hear the starting gun in it. For the first time in history, the baseline is solved. Which means the only question left is the one every generation before yours was too buried to ask. What are you actually here to build.

Dustin

63,293 views • 4 months ago

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"
2:21

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,249 views • 8 months ago

Elon Musk was asked what happens to people when the machines no longer need them. He didn’t soften it. Musk: “There will be fewer and fewer jobs that a robot cannot do better. These are not things I wish would happen. They probably will.” Sit with that second sentence. He is not celebrating. He is not selling a vision. He is telling you what he believes is inevitable and admitting he wishes it weren’t. That is not optimism. That is a confession. Most people are still arguing over whether this is real. Whether it’s their job or someone else’s. Whether the timeline is years away or decades. Musk isn’t arguing. He resolved it. And it bothers him. Musk: “I think ultimately we will have to have some kind of universal basic income. I don’t think we’re going to have a choice.” Not a political position. Not a utopian proposal. A concession. We are building something so capable that human labor stops being a required input to the economy. The machine does not need rest. It does not need a salary. It does not call in sick. It does not ask for a raise. And it improves every single month. The jobs that feel safe right now are not safe because they are irreplaceable. They feel safe because the technology hasn’t fully arrived yet. It’s arriving. Musk: “How do people then have meaning? If there’s not a need for your labor, what’s the meaning? Do you feel useless?” He said that is the harder problem. Not the economics. Not the policy. Not how you fund UBI or make it hold. The harder problem is what happens to a person who built their entire identity around being needed. That is most people. You were trained from childhood to believe your value is what you produce. That your worth is what you earn. That rest is something you survive the week to reach, not something you deserve simply by existing. When the machine removes the need for your labor, that belief does not update. It breaks. The people least prepared for that moment are the ones who worked the hardest. The ones who took the most pride in being indispensable. The ones who made work the whole answer. Losing the job is survivable. Losing the reason to get up is not. That is what Musk is actually asking. Not how do we pay people. How do we build a world where people still feel like they matter when the economy no longer needs them. Nobody in power is seriously working on that answer. The machine didn’t wait.
1:16

Elon Musk was asked what happens to people when the machines no longer need them. He didn’t soften it. Musk: “There will be fewer and fewer jobs that a robot cannot do better. These are not things I wish would happen. They probably will.” Sit with that second sentence. He is not celebrating. He is not selling a vision. He is telling you what he believes is inevitable and admitting he wishes it weren’t. That is not optimism. That is a confession. Most people are still arguing over whether this is real. Whether it’s their job or someone else’s. Whether the timeline is years away or decades. Musk isn’t arguing. He resolved it. And it bothers him. Musk: “I think ultimately we will have to have some kind of universal basic income. I don’t think we’re going to have a choice.” Not a political position. Not a utopian proposal. A concession. We are building something so capable that human labor stops being a required input to the economy. The machine does not need rest. It does not need a salary. It does not call in sick. It does not ask for a raise. And it improves every single month. The jobs that feel safe right now are not safe because they are irreplaceable. They feel safe because the technology hasn’t fully arrived yet. It’s arriving. Musk: “How do people then have meaning? If there’s not a need for your labor, what’s the meaning? Do you feel useless?” He said that is the harder problem. Not the economics. Not the policy. Not how you fund UBI or make it hold. The harder problem is what happens to a person who built their entire identity around being needed. That is most people. You were trained from childhood to believe your value is what you produce. That your worth is what you earn. That rest is something you survive the week to reach, not something you deserve simply by existing. When the machine removes the need for your labor, that belief does not update. It breaks. The people least prepared for that moment are the ones who worked the hardest. The ones who took the most pride in being indispensable. The ones who made work the whole answer. Losing the job is survivable. Losing the reason to get up is not. That is what Musk is actually asking. Not how do we pay people. How do we build a world where people still feel like they matter when the economy no longer needs them. Nobody in power is seriously working on that answer. The machine didn’t wait.

Dustin

247,138 views • 4 months ago

Mo Gawdat believes up to 30% of jobs in certain sectors could disappear by 2028. That stopped me in my tracks. Mo was one of the first people to come on this podcast and warn me about AI, long before most of the world was talking about it. At the time, it felt early. Now, it feels like the world is catching up to what he was seeing. I’m still trying to understand what AI actually means for our lives. Not just whether it can write emails, create images or make us more productive. I mean what it does to jobs. What it does to power. What it does to education. What it does to human connection… That’s why I wanted to have this discussion with Mo again. What makes Mo worth listening to is that he saw these systems inside Google years before most of us had even heard the term AI. His book *Scary Smart* now feels like it was written for this exact moment. Let me explain why this discussion matters. Mo believes we’re not just entering an AI revolution. We’re entering a period where AI, robotics, economics, surveillance, digital currencies and global instability are all colliding at the same time. That’s a lot for any of us to process. We spoke about: - The jobs Mo believes are most at risk from AI. - Why he believes that AI is actually underhyped! - The mistake almost everyone is making with ChatGPT. - The prediction that changed even his own view of the future. The part that stayed with me was this idea that human connection may become the real currency. Because if AI can produce the information, write the report, analyse the data, then what is left? I don’t think this conversation gives neat answers. That’s probably why it’s worth watching. It helped me think more honestly about what’s coming.
1:29

Mo Gawdat believes up to 30% of jobs in certain sectors could disappear by 2028. That stopped me in my tracks. Mo was one of the first people to come on this podcast and warn me about AI, long before most of the world was talking about it. At the time, it felt early. Now, it feels like the world is catching up to what he was seeing. I’m still trying to understand what AI actually means for our lives. Not just whether it can write emails, create images or make us more productive. I mean what it does to jobs. What it does to power. What it does to education. What it does to human connection… That’s why I wanted to have this discussion with Mo again. What makes Mo worth listening to is that he saw these systems inside Google years before most of us had even heard the term AI. His book *Scary Smart* now feels like it was written for this exact moment. Let me explain why this discussion matters. Mo believes we’re not just entering an AI revolution. We’re entering a period where AI, robotics, economics, surveillance, digital currencies and global instability are all colliding at the same time. That’s a lot for any of us to process. We spoke about: - The jobs Mo believes are most at risk from AI. - Why he believes that AI is actually underhyped! - The mistake almost everyone is making with ChatGPT. - The prediction that changed even his own view of the future. The part that stayed with me was this idea that human connection may become the real currency. Because if AI can produce the information, write the report, analyse the data, then what is left? I don’t think this conversation gives neat answers. That’s probably why it’s worth watching. It helped me think more honestly about what’s coming.

Steven Bartlett

27,882 views • 2 months ago

*Singularity Tingles Intensify* The singularity feels more real than ever and the vibe has officially shifted. We are hearing that 2026 is the year AI really starts to accelerate science itself. If you are reading this you might feel like you are constantly two or three years ahead of the curve. That is a lonely place to be because we are living through an epistemic seismic shift that our chimpanzee brains are not evolved to handle. Our brains are designed for a local and geometric world not a global and exponential one. We literally do not have the neural machinery to understand hyper objects like the singularity so we fall back on normalcy bias to keep us sane. Consider this wild stat almost half of Americans who have used ChatGPT still believe it is just fetching responses from a database rather than generating them in real time. That is the gap between the consensus reality and what is actually happening. While people argue over whether AI is just a search engine we are seeing models like Claude write nearly 100% of the code for their own next iterations. We have basically reached AGI for coding. I want to introduce a concept called Cognitive Lacuna. You know cognitive dissonance where two conflicting ideas cause pain. A cognitive lacuna is different it is when you know there is a shape missing from what you are trying to understand. You can sense the gradient of the answer but you cannot name it yet. That is where human intuition lives and that is the next frontier for AI coherence. We are moving from chatbots that hallucinate to intelligences that can identify exactly what is missing from our scientific understanding. If you feel disoriented right now that just means you are paying attention. Are you feeling the acceleration picking up for 2026 or do you think the curve is flattening?
27:39

*Singularity Tingles Intensify* The singularity feels more real than ever and the vibe has officially shifted. We are hearing that 2026 is the year AI really starts to accelerate science itself. If you are reading this you might feel like you are constantly two or three years ahead of the curve. That is a lonely place to be because we are living through an epistemic seismic shift that our chimpanzee brains are not evolved to handle. Our brains are designed for a local and geometric world not a global and exponential one. We literally do not have the neural machinery to understand hyper objects like the singularity so we fall back on normalcy bias to keep us sane. Consider this wild stat almost half of Americans who have used ChatGPT still believe it is just fetching responses from a database rather than generating them in real time. That is the gap between the consensus reality and what is actually happening. While people argue over whether AI is just a search engine we are seeing models like Claude write nearly 100% of the code for their own next iterations. We have basically reached AGI for coding. I want to introduce a concept called Cognitive Lacuna. You know cognitive dissonance where two conflicting ideas cause pain. A cognitive lacuna is different it is when you know there is a shape missing from what you are trying to understand. You can sense the gradient of the answer but you cannot name it yet. That is where human intuition lives and that is the next frontier for AI coherence. We are moving from chatbots that hallucinate to intelligences that can identify exactly what is missing from our scientific understanding. If you feel disoriented right now that just means you are paying attention. Are you feeling the acceleration picking up for 2026 or do you think the curve is flattening?

David Shapiro (L/0)

27,956 views • 7 months ago