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AI pricing is a function of 2 variables: agent autonomy & value attribution • Low autonomy, low attribution → seat-based Slack, early GitHub • Low autonomy, high attribution → hybrid (seats + credits) Cursor, Clay • High autonomy, low attribution → usage-based Twilio • High autonomy, high attribution →... show more

Dara

10,905 subscribers

40,641 次观看 • 9 个月前 •via X (Twitter)

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Is Per Seat Pricing Dead in a World of AI: "There are two logical replacements: 1. Value-based pricing. 2. Consumption-based pricing. Talk to customers about consumption based pricing. They do not love it. The problem with value based pricing is customer and vendor have to align on the attribution of value." How do you think about this and the future of pricing in SaaS Marc Benioff Des Traynor Bret Taylor Jesse Zhang

Is Per Seat Pricing Dead in a World of AI: "There are two logical replacements: 1. Value-based pricing. 2. Consumption-based pricing. Talk to customers about consumption based pricing. They do not love it. The problem with value based pricing is customer and vendor have to align on the attribution of value." How do you think about this and the future of pricing in SaaS Marc Benioff Des Traynor Bret Taylor Jesse Zhang

Harry Stebbings

23,605 次观看 • 11 个月前

Introducing Antioch Agent. For the first time, simulate the full physical AI stack in a closed agentic loop, entirely from the browser. Onboard your robot, build high-fidelity simulation scenes, and test in hours what takes weeks in the field. Develop physical autonomy at the speed of software, with Antioch Agent.

Introducing Antioch Agent. For the first time, simulate the full physical AI stack in a closed agentic loop, entirely from the browser. Onboard your robot, build high-fidelity simulation scenes, and test in hours what takes weeks in the field. Develop physical autonomy at the speed of software, with Antioch Agent.

Antioch

937,775 次观看 • 3 个月前

Are UAP Orbs Adversary Drones? It is plausible that the majority of the low-speed, formation-flying, radar-quiet orbs are genuinely human-made drones, while the outlier high-speed or high-radar-cross-section cases within the same dataset represent either measurement error or an attribution mistake, whereby a fast-moving object like a missile or aircraft momentarily conflated with a slower drone swarm in the same scene. Better digital data is needed to decide whether any of the UAP orbs represent a new class of objects of non-human origin.

Are UAP Orbs Adversary Drones? It is plausible that the majority of the low-speed, formation-flying, radar-quiet orbs are genuinely human-made drones, while the outlier high-speed or high-radar-cross-section cases within the same dataset represent either measurement error or an attribution mistake, whereby a fast-moving object like a missile or aircraft momentarily conflated with a slower drone swarm in the same scene. Better digital data is needed to decide whether any of the UAP orbs represent a new class of objects of non-human origin.

Professor Avi Loeb

16,808 次观看 • 27 天前

🏙I have mostly been working on Tools lately, but I have also been blocking out a bit on a test level, adding various paths, high and low, based on your mastery of the controls. All rough and nothing final! #gamedev #indiedev #screenshotsaturday #blocktober

🏙I have mostly been working on Tools lately, but I have also been blocking out a bit on a test level, adding various paths, high and low, based on your mastery of the controls. All rough and nothing final! #gamedev #indiedev #screenshotsaturday #blocktober

MortMort

841,706 次观看 • 2 年前

🚀After Years of building battle-tested autonomy for drones, we’re excited to make JETCORE-FC publicly available ! Our flagship compute module along with Flight control carrier board designed for real-time perception, autonomy, and edge AI workloads. Built around NVIDIA Jetson, JETCORE-FC powers our own drones that fly without GPS, without radio, and without compromise. Now, we’re bringing the same power to the hands of developers, researchers, and system integrators. 🔧 Plug-and-play with: High-speed interfaces for camera, LIDAR, and IMU Ruggedized I/O and low-latency networking Full support for ROS, PX4, and custom Linux pipelines Whether you’re building next-gen robotics, defense-grade UGVs, or intelligent drones, JETCORE-FC gives you the reliability and performance we trust in the field. 🛒 Get yours now → #Vecros #JETCORE #EdgeAI #NVIDIAJetson #Robotics #Autonomy #Drones #AI #GPSTech #DroneComputing #SpatialAI NVIDIA NVIDIA Robotics Rashree Besta prem sai RobuIN

🚀After Years of building battle-tested autonomy for drones, we’re excited to make JETCORE-FC publicly available ! Our flagship compute module along with Flight control carrier board designed for real-time perception, autonomy, and edge AI workloads. Built around NVIDIA Jetson, JETCORE-FC powers our own drones that fly without GPS, without radio, and without compromise. Now, we’re bringing the same power to the hands of developers, researchers, and system integrators. 🔧 Plug-and-play with: High-speed interfaces for camera, LIDAR, and IMU Ruggedized I/O and low-latency networking Full support for ROS, PX4, and custom Linux pipelines Whether you’re building next-gen robotics, defense-grade UGVs, or intelligent drones, JETCORE-FC gives you the reliability and performance we trust in the field. 🛒 Get yours now → #Vecros #JETCORE #EdgeAI #NVIDIAJetson #Robotics #Autonomy #Drones #AI #GPSTech #DroneComputing #SpatialAI NVIDIA NVIDIA Robotics Rashree Besta prem sai RobuIN

VECROS

17,278 次观看 • 1 年前

Most founders accidentally train their customers never to pay them. Madhavan Ramanujam (the "pricing guru" of Silicon Valley - having worked with LinkedIn, Uber, and 30+ unicorns) calls it the 20/80 Pricing Trap • 20% of your features drive 80% of the willingness to pay • Founders give that 20% away for free to gain distribution • You are left trying to monetize the remaining 80% of features - the ones users don't actually value. The result? You build a charity, not a business. In this week's episode of The Library of Minds, we discuss the science of monetization and deconstruct how to architect ‘Profitable Growth’ - the core framework from his new book, Scaling Innovation. 03:33 - Netflix vs Blockbuster: The Pricing Decision That Changed Tech 08:06 - Why Most Startups Get Pricing Wrong 11:39 - Freemium vs Paid: When Free Destroys Value 15:38 - Pricing Models Matter More Than Price 16:28 - The AI Pricing Framework: Autonomy vs Attribution 21:49 - The Biggest Pricing Mistake Ever 25:05 - Why Steve Jobs Was a Pricing Genius 26:44 - Behavioral Pricing: How Founders 10× Deals Without Changing Product 31:02 - Data vs Conviction: How Great Founders Make Pricing Decisions

Most founders accidentally train their customers never to pay them. Madhavan Ramanujam (the "pricing guru" of Silicon Valley - having worked with LinkedIn, Uber, and 30+ unicorns) calls it the 20/80 Pricing Trap • 20% of your features drive 80% of the willingness to pay • Founders give that 20% away for free to gain distribution • You are left trying to monetize the remaining 80% of features - the ones users don't actually value. The result? You build a charity, not a business. In this week's episode of The Library of Minds, we discuss the science of monetization and deconstruct how to architect ‘Profitable Growth’ - the core framework from his new book, Scaling Innovation. 03:33 - Netflix vs Blockbuster: The Pricing Decision That Changed Tech 08:06 - Why Most Startups Get Pricing Wrong 11:39 - Freemium vs Paid: When Free Destroys Value 15:38 - Pricing Models Matter More Than Price 16:28 - The AI Pricing Framework: Autonomy vs Attribution 21:49 - The Biggest Pricing Mistake Ever 25:05 - Why Steve Jobs Was a Pricing Genius 26:44 - Behavioral Pricing: How Founders 10× Deals Without Changing Product 31:02 - Data vs Conviction: How Great Founders Make Pricing Decisions

Dara

215,786 次观看 • 9 个月前

The goal of modern education policy is not “helping every kid achieve their potential”, but rather “closing the gap” between high achievers and low achievers. Unfortunately, this means that many high-achieving students are intentionally slowed down, and held back to allow their classmates to catch up. Mentava Reading 🐮 is building software to ensure high-achieving students can have their learning needs met, starting with a software-based tutor that teaches preschoolers to read.

The goal of modern education policy is not “helping every kid achieve their potential”, but rather “closing the gap” between high achievers and low achievers. Unfortunately, this means that many high-achieving students are intentionally slowed down, and held back to allow their classmates to catch up. Mentava Reading 🐮 is building software to ensure high-achieving students can have their learning needs met, starting with a software-based tutor that teaches preschoolers to read.

Niels Hoven 🐮

493,912 次观看 • 2 年前

🚨BREAKING 🦠 On Friday, the US CDC arbitrarily shifted higher COVlD levels into lower categories depicted with cooler colors. Much of what was Very High is now "High." What was High is now "Moderate" or "Low." Low is "Very Low." 🔹2 states/territories artificially shifted from High to Moderate (MS, GU) 🔹5 shifted from Moderate to Low (CA, TX, AK, WA, NV) 🔹4 shifted from Low to Very Low (AZ, AR, LA, NC) During intense surges, states will rarely get classified as Very High anymore. Presently, transmission is rising across the US, with a 75% chance of a wave, but states will be depicted in blue (Very Low) much longer, making communities less prepared. #Bluewashing Normally, when the CDC makes a methodology change, they flag it prominently on each of their COVlD monitoring pages. This time, there was no alert and no explanation. Major changes were confined to COVlD, not Influenza A, and not RSV. The questionable change was made in the middle of rising transmission across most of the country; 15 states already have COVlD outbreaks, and others may soon join the ranks. These are serious ethics concerns that warrant investigative journalism. These issues are aggravated by delayed reporting at High/Very High monitoring sites. Sites with 1-week reporting delays get excluded when the CDC calculates current levels. For example, only 16% of the sites online in Texas (3 of 19) report High/Very High levels, but 90% of those with a 1-week reporting delay (18 of 20) have High/Very High levels, leading the state to get classified as "Low" when really High. These issues snowball and can easily push a state down 1-2 categories when experiencing significant COVlD outbreaks. The good news is that the individual wastewater monitoring site data are still valid. The bad news is that the CDC maps and level descriptions will now mislead the casual user. Think: "Good data, bad use of data." At PMC, we told you that if there was any funny business at the CDC, we would warn you right away. We diagnosed the issue immediately and will still report accurate COVlD information. Expect further national news coverage this week. Journalists are welcome to reach out.

🚨BREAKING 🦠 On Friday, the US CDC arbitrarily shifted higher COVlD levels into lower categories depicted with cooler colors. Much of what was Very High is now "High." What was High is now "Moderate" or "Low." Low is "Very Low." 🔹2 states/territories artificially shifted from High to Moderate (MS, GU) 🔹5 shifted from Moderate to Low (CA, TX, AK, WA, NV) 🔹4 shifted from Low to Very Low (AZ, AR, LA, NC) During intense surges, states will rarely get classified as Very High anymore. Presently, transmission is rising across the US, with a 75% chance of a wave, but states will be depicted in blue (Very Low) much longer, making communities less prepared. #Bluewashing Normally, when the CDC makes a methodology change, they flag it prominently on each of their COVlD monitoring pages. This time, there was no alert and no explanation. Major changes were confined to COVlD, not Influenza A, and not RSV. The questionable change was made in the middle of rising transmission across most of the country; 15 states already have COVlD outbreaks, and others may soon join the ranks. These are serious ethics concerns that warrant investigative journalism. These issues are aggravated by delayed reporting at High/Very High monitoring sites. Sites with 1-week reporting delays get excluded when the CDC calculates current levels. For example, only 16% of the sites online in Texas (3 of 19) report High/Very High levels, but 90% of those with a 1-week reporting delay (18 of 20) have High/Very High levels, leading the state to get classified as "Low" when really High. These issues snowball and can easily push a state down 1-2 categories when experiencing significant COVlD outbreaks. The good news is that the individual wastewater monitoring site data are still valid. The bad news is that the CDC maps and level descriptions will now mislead the casual user. Think: "Good data, bad use of data." At PMC, we told you that if there was any funny business at the CDC, we would warn you right away. We diagnosed the issue immediately and will still report accurate COVlD information. Expect further national news coverage this week. Journalists are welcome to reach out.

Mike Hoerger, PhD MSCR MBA

405,730 次观看 • 1 个月前

Hybrid models efficiently route complex tasks to larger cloud models, but how do you go about creating one? 🤔 Cactus solved this by post-training Gemma 4 to return a confidence score for every prompt. Process the prompt locally when confidence is high, route to a larger model when confidence is low. Routing only happens 15%-55% of time based on domain benchmarks.

Hybrid models efficiently route complex tasks to larger cloud models, but how do you go about creating one? 🤔 Cactus solved this by post-training Gemma 4 to return a confidence score for every prompt. Process the prompt locally when confidence is high, route to a larger model when confidence is low. Routing only happens 15%-55% of time based on domain benchmarks.

Google Gemma

25,195 次观看 • 1 个月前

Elon Musk: Autonomy and Optimus will actually happen. “At a very high level, I'd say that autonomy is an insanely fundamental breakthrough. And no one is even close to Tesla for solving generalized autonomy or generalized self-driving vehicles. No one's even close. With self-driving, the car becomes roughly five times more useful, but it costs the same to build. Can you imagine what would happen if a company were doing a 25% to 30% percent gross margins, but suddenly that same thing was five times more valuable. What would that do to the value of Tesla ? And the value of that car? It boggles the mind, actually. If you actually do the math on that, it's insane. There's also the Optimus program, which is our humanoid robot, where we will leverage our manufacturing expertise and the intelligence we've developed for self-driving to have a useful humanoid robot. The economy is fundamentally GDP per capita times capita. If you no longer have a constraint on capita because of a useful humanoid robot, it's not clear that there's any limit to the size of the economy. And these things will actually happen.” Baron Investment Conference, 2022

Elon Musk: Autonomy and Optimus will actually happen. “At a very high level, I'd say that autonomy is an insanely fundamental breakthrough. And no one is even close to Tesla for solving generalized autonomy or generalized self-driving vehicles. No one's even close. With self-driving, the car becomes roughly five times more useful, but it costs the same to build. Can you imagine what would happen if a company were doing a 25% to 30% percent gross margins, but suddenly that same thing was five times more valuable. What would that do to the value of Tesla ? And the value of that car? It boggles the mind, actually. If you actually do the math on that, it's insane. There's also the Optimus program, which is our humanoid robot, where we will leverage our manufacturing expertise and the intelligence we've developed for self-driving to have a useful humanoid robot. The economy is fundamentally GDP per capita times capita. If you no longer have a constraint on capita because of a useful humanoid robot, it's not clear that there's any limit to the size of the economy. And these things will actually happen.” Baron Investment Conference, 2022

ELON CLIPS

34,124 次观看 • 1 年前

Many startups find early pull, but only a few break out. Why? They stop at product/market fit and never find business model fit. Business model fit isn’t just about how much you charge for your product, but HOW you charge for it, and how you align customer value with your pricing. There’s a lot of advice out there about seat-based vs. usage-based vs. outcome-based pricing, but all of that is just noise. The search for business model fit is as specific to your company as the search for product market fit. You need to align your acquisition strategy and monetization strategy with your customer and product. It’s all one interconnected system. Take Slack for example: many associate Slack’s early success with its fun, creative design and fresh approach to communication. That’s what drew people to the product. But a big part of what made Slack a breakout success was business model fit. Freemium wasn’t just an acquisition tactic, it was essential to driving the revenue flywheel, viral expansion, and adoption in the enterprise. Obvious in retrospect, but controversial and against the grain of what others were doing at the time. The best founders don’t just chase product/market fit, they design a business model to make their product unstoppable. If that’s you, we want to meet you speedrun - link in replies

Many startups find early pull, but only a few break out. Why? They stop at product/market fit and never find business model fit. Business model fit isn’t just about how much you charge for your product, but HOW you charge for it, and how you align customer value with your pricing. There’s a lot of advice out there about seat-based vs. usage-based vs. outcome-based pricing, but all of that is just noise. The search for business model fit is as specific to your company as the search for product market fit. You need to align your acquisition strategy and monetization strategy with your customer and product. It’s all one interconnected system. Take Slack for example: many associate Slack’s early success with its fun, creative design and fresh approach to communication. That’s what drew people to the product. But a big part of what made Slack a breakout success was business model fit. Freemium wasn’t just an acquisition tactic, it was essential to driving the revenue flywheel, viral expansion, and adoption in the enterprise. Obvious in retrospect, but controversial and against the grain of what others were doing at the time. The best founders don’t just chase product/market fit, they design a business model to make their product unstoppable. If that’s you, we want to meet you speedrun - link in replies

Fareed Mosavat

24,966 次观看 • 1 年前

I recently came across this video of an analyst claiming a specific Situational Analysis pattern, with more than 90% accuracy. Instead of taking it at face value, I fed the video and years of historical Nifty data into Gemini AI. By the time my coffee was ready, the AI backtested 20+ years of data. ✅ Scenario 1 - If the high price on Friday is lower than the high price on Thursday. The low price reached on Friday will be revisited during the regular trading session on Monday. Accuracy of this condition is 57.2%. Not 90% as mentioned by the expert, but a statistically significant advantage you can actually build a strategy around. ❌ Scenario 2: If the high price on Wednesday is lower than the high price on Monday. The low price reached on Wednesday will be revisited on Thursday. A literal 50.8% accuracy only. We live in an era where you can validate any trading idea instantly.

I recently came across this video of an analyst claiming a specific Situational Analysis pattern, with more than 90% accuracy. Instead of taking it at face value, I fed the video and years of historical Nifty data into Gemini AI. By the time my coffee was ready, the AI backtested 20+ years of data. ✅ Scenario 1 - If the high price on Friday is lower than the high price on Thursday. The low price reached on Friday will be revisited during the regular trading session on Monday. Accuracy of this condition is 57.2%. Not 90% as mentioned by the expert, but a statistically significant advantage you can actually build a strategy around. ❌ Scenario 2: If the high price on Wednesday is lower than the high price on Monday. The low price reached on Wednesday will be revisited on Thursday. A literal 50.8% accuracy only. We live in an era where you can validate any trading idea instantly.

Kirubakaran Rajendran

33,176 次观看 • 7 个月前

For web design Fable High is REALLY enough. First example is using low all up to max on the fifth Prompt was "Build an Awwwards-level responsive website for “Lumenform”, a fictional creative AI studio. Use generated assets, multiple if needed. Make it cinematic, premium, original, with parallax, scroll-based motion, strong typography, layered depth, polished mobile, and clean interactions."

For web design Fable High is REALLY enough. First example is using low all up to max on the fifth Prompt was "Build an Awwwards-level responsive website for “Lumenform”, a fictional creative AI studio. Use generated assets, multiple if needed. Make it cinematic, premium, original, with parallax, scroll-based motion, strong typography, layered depth, polished mobile, and clean interactions."

Leon Lin

34,621 次观看 • 3 个月前

‘24 | RHP | Eli Crecelius | (Valley View Blazer Baseball | Arkansas Baseball) Crecelius gave up an early run in the first but then settled in nicely as he went 6.0 IP w/ 11K and just 1BB. Easy & clean, repeated delivery, but the pitch-ability is the separator as he commanded a 4-pitch mix that was in the zone in all counts. FB (T 89 w/ some cut at times, low usage rate but commanded on both sides of plate) CB (12/6 shape and high usage rate early in counts) Sl (high spin and late life; high usage rate late in counts as S&M) And CH ( ASR & depth when down in zone) Shooter Hunt

‘24 | RHP | Eli Crecelius | (Valley View Blazer Baseball | Arkansas Baseball) Crecelius gave up an early run in the first but then settled in nicely as he went 6.0 IP w/ 11K and just 1BB. Easy & clean, repeated delivery, but the pitch-ability is the separator as he commanded a 4-pitch mix that was in the zone in all counts. FB (T 89 w/ some cut at times, low usage rate but commanded on both sides of plate) CB (12/6 shape and high usage rate early in counts) Sl (high spin and late life; high usage rate late in counts as S&M) And CH ( ASR & depth when down in zone) Shooter Hunt

Prep Baseball Arkansas

11,110 次观看 • 2 年前

"User-based pricing is a thing of the past." How Databricks found out the hard way: "When we started selling Databricks, we were doing $15k, $18k deals annual deals. New rule became: no deals less than my monthly Uber bill. We were adding tons of value. But because we're usage-based, a lot of the use cases weren't driving a lot of usage. So we started asking, how do we capture some of that value? Your proper pricing model is price equals value. If your price is over the value, no one buys. If it's under, you're leaving money on the table. So early days we added a platform fee. Then user fees, all these things to push our price points up to match the value. And one thing that wasn't intuitive: once we started charging for users, people started restricting who could use the product. Which drives down usage. So I said, okay, let's get rid of user pricing. All the users are free. And all of a sudden usage took off. Because now everybody can use it, everybody can get value out of it. Data's growing. Queries are growing. The number of people asking queries is growing. The number of agents asking queries is growing. All of it is tied to usage now. I think user-based pricing is a thing of the past, honestly."

"User-based pricing is a thing of the past." How Databricks found out the hard way: "When we started selling Databricks, we were doing $15k, $18k deals annual deals. New rule became: no deals less than my monthly Uber bill. We were adding tons of value. But because we're usage-based, a lot of the use cases weren't driving a lot of usage. So we started asking, how do we capture some of that value? Your proper pricing model is price equals value. If your price is over the value, no one buys. If it's under, you're leaving money on the table. So early days we added a platform fee. Then user fees, all these things to push our price points up to match the value. And one thing that wasn't intuitive: once we started charging for users, people started restricting who could use the product. Which drives down usage. So I said, okay, let's get rid of user pricing. All the users are free. And all of a sudden usage took off. Because now everybody can use it, everybody can get value out of it. Data's growing. Queries are growing. The number of people asking queries is growing. The number of agents asking queries is growing. All of it is tied to usage now. I think user-based pricing is a thing of the past, honestly."

Turner Novak 🍌🧢

58,595 次观看 • 11 天前

"A parcel with snacks has been delivered for Flexion. Retrieve it using the stairs and come up using the elevator. Then unpack it and place the items into the empty drawer on the shelf in the snack area." One instruction. No human operator. Everything that follows is autonomous. Today we're introducing Reflect v1.0, our robotics intelligence platform for long-horizon work. From a single natural-language command, the robot understands the task, navigates a multi-floor building, calls elevators, handles doors, uses tools to unpack a box, and puts the items away. The biggest shift in v1.0 is that we use reinforcement learning across every layer, from low-level control to high-level reasoning. Long-horizon autonomy is unforgiving. The robot must recover on its own when things don't go to plan because in the real world, they never do. Combining reasoning, perception, physical execution and runtime robustness into a single mission-capable system is the foundation required to solve humanoid autonomy. Our team is just getting started. #HumanoidRobots #Flexion

"A parcel with snacks has been delivered for Flexion. Retrieve it using the stairs and come up using the elevator. Then unpack it and place the items into the empty drawer on the shelf in the snack area." One instruction. No human operator. Everything that follows is autonomous. Today we're introducing Reflect v1.0, our robotics intelligence platform for long-horizon work. From a single natural-language command, the robot understands the task, navigates a multi-floor building, calls elevators, handles doors, uses tools to unpack a box, and puts the items away. The biggest shift in v1.0 is that we use reinforcement learning across every layer, from low-level control to high-level reasoning. Long-horizon autonomy is unforgiving. The robot must recover on its own when things don't go to plan because in the real world, they never do. Combining reasoning, perception, physical execution and runtime robustness into a single mission-capable system is the foundation required to solve humanoid autonomy. Our team is just getting started. #HumanoidRobots #Flexion

Flexion

142,662 次观看 • 2 个月前

We have a serious global problem “The population collapse of high IQ individuals is probably the scariest part of the fertility crisis because high IQ people are disproportionately the ones innovating and progressing society forward” If you look at high IQ countries, the population is collapsing. But low IQ countries are breeding like crazy and they are being flooded into high IQ countries He goes over IQ distribution chart by country In the low IQ country example “you would need 2 billion people to come up with the same 2 million geniuses, if we're qualifying 1 in 20 as genius. You need 2 billion people to get to that number, whereas with the high IQ kind of distribution, you only needed a population of a 100 million.” “So when you have this fertility collapse going on in these high IQ nations, you are going to get a collapse in even the absolute number of geniuses that exist in the world, and that paints a bleak picture for the future” Top counties for IQ are Asian, China, Japan and western nations like the United States The bottom 5 countries for IQ in the entire world are Somalia and African countries We are importing our own demise. We should be incentivizing high IQ countries to have more babies not mass importing from low IQ countries. This is a common sense concept that needs to be openly discussed

We have a serious global problem “The population collapse of high IQ individuals is probably the scariest part of the fertility crisis because high IQ people are disproportionately the ones innovating and progressing society forward” If you look at high IQ countries, the population is collapsing. But low IQ countries are breeding like crazy and they are being flooded into high IQ countries He goes over IQ distribution chart by country In the low IQ country example “you would need 2 billion people to come up with the same 2 million geniuses, if we're qualifying 1 in 20 as genius. You need 2 billion people to get to that number, whereas with the high IQ kind of distribution, you only needed a population of a 100 million.” “So when you have this fertility collapse going on in these high IQ nations, you are going to get a collapse in even the absolute number of geniuses that exist in the world, and that paints a bleak picture for the future” Top counties for IQ are Asian, China, Japan and western nations like the United States The bottom 5 countries for IQ in the entire world are Somalia and African countries We are importing our own demise. We should be incentivizing high IQ countries to have more babies not mass importing from low IQ countries. This is a common sense concept that needs to be openly discussed

Wall Street Apes

48,443 次观看 • 20 天前