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Google's former CEO Eric Schmidt on the data strategy behind the next hundred billion dollar companies: He explains that he evaluates startup ideas by looking at 5-year growth trajectories and asking whether there's a more scalable strategy available. Take a founder building an app they want to charge $10...

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104,452 次观看 • 5 个月前 •via X (Twitter)

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How Google’s CEO predicted ChatGPT in 2016 “It’ll use Android/iOS, fast networks, and powerful machine learning… The model where you crowdsource information in, learn it, and then sell it is a highly likely candidate for the next $100 billion company.” As Eric Schmidt explains, it’s much easier to predict what the great platforms will be in five years than the specific companies. Five years before this talk, he predicted that the future will be “apps that are on smartphones, that use Google Maps GPS, and do something useful.” ”What I should’ve said was Uber,” Eric admits. Eric believes one way to generate huge startup ideas is to identify huge technology waves and figure out useful new ways of leveraging them. Jeff Bezos is perhaps the most famous example of this approach. He saw that web usage was growing at 2,300% a year in 1994 and that it could be used to invent a new kind of book store. Video source: Startup Grind (2016)
2:37

How Google’s CEO predicted ChatGPT in 2016 “It’ll use Android/iOS, fast networks, and powerful machine learning… The model where you crowdsource information in, learn it, and then sell it is a highly likely candidate for the next $100 billion company.” As Eric Schmidt explains, it’s much easier to predict what the great platforms will be in five years than the specific companies. Five years before this talk, he predicted that the future will be “apps that are on smartphones, that use Google Maps GPS, and do something useful.” ”What I should’ve said was Uber,” Eric admits. Eric believes one way to generate huge startup ideas is to identify huge technology waves and figure out useful new ways of leveraging them. Jeff Bezos is perhaps the most famous example of this approach. He saw that web usage was growing at 2,300% a year in 1994 and that it could be used to invent a new kind of book store. Video source: Startup Grind (2016)

Startup Archive

11,254 次观看 • 1 年前

Eric Schmidt on how he uses 5-year plans to predict if a startup can become a $100 billion company “If you went to business school, you would’ve been taught: build a great product, organize a sales force, charge a fair price, make the customer happy.” But, as the former CEO of Google explains, that strategy is insufficiently scalable in the Internet era: “It’ll produce a reasonable business, but it’s not going to produce a huge business. It’s just too hard to hire all of those salespeople, work with every customer, and so forth. You have to have a more clever strategy.” Eric continues: “All of the really big companies have invented a new way to access information or a new way to do something [and didn’t require a large salesforce].” Eric argues that lots of the startup ideas he hears are good, but not good enough. He tells these founders to do a plan over the next five years and map your growth rate. Then try to figure out what a more scalable strategy might be. For example, if you’re building an app that you want to charge $10 for, Eric would ask: “Why can’t you give the app away for free and then upsell the users?” Another way to use a five-year plan to determine if your company can be a $100 billion company is to ask yourself what the big platforms will be five years from now and make sure your company is aligned with those platforms. In this interview from 2016, he predicts Android, iOS, and machine learning are the platforms he’d want to be aligned with over the next five years. Video source: Startup Grind (2016)
2:58

Eric Schmidt on how he uses 5-year plans to predict if a startup can become a $100 billion company “If you went to business school, you would’ve been taught: build a great product, organize a sales force, charge a fair price, make the customer happy.” But, as the former CEO of Google explains, that strategy is insufficiently scalable in the Internet era: “It’ll produce a reasonable business, but it’s not going to produce a huge business. It’s just too hard to hire all of those salespeople, work with every customer, and so forth. You have to have a more clever strategy.” Eric continues: “All of the really big companies have invented a new way to access information or a new way to do something [and didn’t require a large salesforce].” Eric argues that lots of the startup ideas he hears are good, but not good enough. He tells these founders to do a plan over the next five years and map your growth rate. Then try to figure out what a more scalable strategy might be. For example, if you’re building an app that you want to charge $10 for, Eric would ask: “Why can’t you give the app away for free and then upsell the users?” Another way to use a five-year plan to determine if your company can be a $100 billion company is to ask yourself what the big platforms will be five years from now and make sure your company is aligned with those platforms. In this interview from 2016, he predicts Android, iOS, and machine learning are the platforms he’d want to be aligned with over the next five years. Video source: Startup Grind (2016)

Startup Archive

58,465 次观看 • 1 年前

Former Google CEO Eric Schmidt explains how he uses 5-year plans to predict if a startup can become a $100B+ company “If you went to business school, you would’ve been taught: build a great product, organize a sales force, charge a fair price, make the customer happy.” But Eric explains that strategy is insufficiently scalable in the Internet era. “It’ll produce a reasonable business, but it’s not going to produce a huge business. It’s just too hard to hire all of those salespeople, work with every customer, and so forth. You have to have a more clever strategy.” He continues: “All of the really big companies have invented a new way to access information or a new way to do something that didn’t require [a large salesforce].” Eric argues that lots of the startup ideas he hears are good, but not good enough. He tells these founders to create a 5-year plan and map their growth rate. Then try to figure out what a more scalable strategy might be. For example, if you’re building an app that you want to charge $10 for, Eric asks: “Why can’t you give the app away for free and then upsell the users?" This is similar to the advice of Peter Thiel who famously asks founders: “How can you achieve your 10 year plan in the next 6 months?” Thinking big and optimizing for scalability is one key factor that separates the ultra successful companies from the rest. Another way to use a five-year plan to determine if your company can be a $100B+ company is to ask yourself what the big platforms will be five years from now and make sure your company is aligned with those platforms. In this interview from 2016 and he predicted that Android, iOS, and machine learning would be the dominant platforms of the next five years.
2:59

Former Google CEO Eric Schmidt explains how he uses 5-year plans to predict if a startup can become a $100B+ company “If you went to business school, you would’ve been taught: build a great product, organize a sales force, charge a fair price, make the customer happy.” But Eric explains that strategy is insufficiently scalable in the Internet era. “It’ll produce a reasonable business, but it’s not going to produce a huge business. It’s just too hard to hire all of those salespeople, work with every customer, and so forth. You have to have a more clever strategy.” He continues: “All of the really big companies have invented a new way to access information or a new way to do something that didn’t require [a large salesforce].” Eric argues that lots of the startup ideas he hears are good, but not good enough. He tells these founders to create a 5-year plan and map their growth rate. Then try to figure out what a more scalable strategy might be. For example, if you’re building an app that you want to charge $10 for, Eric asks: “Why can’t you give the app away for free and then upsell the users?" This is similar to the advice of Peter Thiel who famously asks founders: “How can you achieve your 10 year plan in the next 6 months?” Thinking big and optimizing for scalability is one key factor that separates the ultra successful companies from the rest. Another way to use a five-year plan to determine if your company can be a $100B+ company is to ask yourself what the big platforms will be five years from now and make sure your company is aligned with those platforms. In this interview from 2016 and he predicted that Android, iOS, and machine learning would be the dominant platforms of the next five years.

Startup Archive

611,873 次观看 • 2 年前

Google is a vaccine company?! "One thing people need to know, is that Google is a vaccine company. Google's parent company alphabet and Verily have this this a series of sister subsidiaries of Google's, actually manufactured vaccines flu vaccines and other vaccines and they're making and they're partnering with all the pharmaceutical companies. So Google has a seven hundred and sixty one million dollar partnership with Glaxo and what they're doing is they're harvesting medical information. That's a huge part of their business and then they're selling it to the pharmaceutical companies. But they're partnering with pharmaceutical companies that they can use that medical they can tell what you're buying habits are they can hear you cough listening to the theory and that all of these, you know, the surveillance systems they're erecting and the informational systems and how we use them and what we purchase is Is all turned into sellable data. Now going to the vaccine companies and the big pharmaceutical companies and they're partnered with them. They're really just part of the big part of the industry."
1:12

Google is a vaccine company?! "One thing people need to know, is that Google is a vaccine company. Google's parent company alphabet and Verily have this this a series of sister subsidiaries of Google's, actually manufactured vaccines flu vaccines and other vaccines and they're making and they're partnering with all the pharmaceutical companies. So Google has a seven hundred and sixty one million dollar partnership with Glaxo and what they're doing is they're harvesting medical information. That's a huge part of their business and then they're selling it to the pharmaceutical companies. But they're partnering with pharmaceutical companies that they can use that medical they can tell what you're buying habits are they can hear you cough listening to the theory and that all of these, you know, the surveillance systems they're erecting and the informational systems and how we use them and what we purchase is Is all turned into sellable data. Now going to the vaccine companies and the big pharmaceutical companies and they're partnered with them. They're really just part of the big part of the industry."

Bridgett Fertig

49,678 次观看 • 6 个月前

Elon Musk on the 3 factors that will determine the winner in AI “Even a small player that is successful in AI will be worth a lot because they’ll contribute so much in productivity to the economy,” Elon explains. “There will be many companies that are worth sustainably several hundred billion dollars. So the next question becomes, How do we [xAI] achieve the lead?” Elon believes it will come down to three things: 1. Are you able to attract the best talent? 2. Are you able to bring more GPUs online faster than anyone else? 3. Unique access to data And uses these three factors to make the case for xAI winning the AI race: “We’ve already demonstrated that we can [bring AI hardware online faster than anyone else]. Jensen Huang himself said that he was blown away by how fast xAI build the Colossus data center . . . And for [unique access to data] we’ve got the X system — formerly the Twitter system — which is by far the best source of real-time data in the world.” He continues: “Those are some pretty significant assets, and I think we’re going to come up with some very innovative ideas . . . I think we’ll make some moves that are not on the chessboard, that people don’t anticipate.”
2:17

Elon Musk on the 3 factors that will determine the winner in AI “Even a small player that is successful in AI will be worth a lot because they’ll contribute so much in productivity to the economy,” Elon explains. “There will be many companies that are worth sustainably several hundred billion dollars. So the next question becomes, How do we [xAI] achieve the lead?” Elon believes it will come down to three things: 1. Are you able to attract the best talent? 2. Are you able to bring more GPUs online faster than anyone else? 3. Unique access to data And uses these three factors to make the case for xAI winning the AI race: “We’ve already demonstrated that we can [bring AI hardware online faster than anyone else]. Jensen Huang himself said that he was blown away by how fast xAI build the Colossus data center . . . And for [unique access to data] we’ve got the X system — formerly the Twitter system — which is by far the best source of real-time data in the world.” He continues: “Those are some pretty significant assets, and I think we’re going to come up with some very innovative ideas . . . I think we’ll make some moves that are not on the chessboard, that people don’t anticipate.”

Startup Archive

41,243 次观看 • 7 个月前

#WATCH | India AI Impact Summit 2026 | Delhi: Founder Chairman and CEO of Sampark Foundation & former CEO of HCL Technologies, Vineet Nayar says, "...From an employment point of view I think it is very important for us to understand that Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming. Therefore, the question is how do we create employment in this environment, and that employment comes from mass scale startups, which is what this government has already doing. So, how do we create new sets of people who are trying to solve new sets of problems not new sets of technology and if we do that we will get it right. I think we as Indians have to be very careful on who does data belong to and that is the debate we have a problem with. The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products, so therefore we do not have SLMs and LLMs which are world-class. On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that or should we not allowed that? But on the other side if we don't allow that then we have the data but we don't have the LLM models. So, how do we encourage technology completely to develop the LLM models. This needs radicals strategic thinking and a very important aspect otherwise we will either give up a data. So, I think it's a very critical aspect for us to think about - who does this data belong, what is the kind of incentives we are going to give to develop LLM technologies or SLM technologies fast so that we train on our data otherwise an LLM will come in with our data and we'll immediately see return and we'll celebrate and we will do all these kind of press releases but the India will lose a competitive advantage on something which is very critical for the next decade."
5:37

#WATCH | India AI Impact Summit 2026 | Delhi: Founder Chairman and CEO of Sampark Foundation & former CEO of HCL Technologies, Vineet Nayar says, "...From an employment point of view I think it is very important for us to understand that Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming. Therefore, the question is how do we create employment in this environment, and that employment comes from mass scale startups, which is what this government has already doing. So, how do we create new sets of people who are trying to solve new sets of problems not new sets of technology and if we do that we will get it right. I think we as Indians have to be very careful on who does data belong to and that is the debate we have a problem with. The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products, so therefore we do not have SLMs and LLMs which are world-class. On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that or should we not allowed that? But on the other side if we don't allow that then we have the data but we don't have the LLM models. So, how do we encourage technology completely to develop the LLM models. This needs radicals strategic thinking and a very important aspect otherwise we will either give up a data. So, I think it's a very critical aspect for us to think about - who does this data belong, what is the kind of incentives we are going to give to develop LLM technologies or SLM technologies fast so that we train on our data otherwise an LLM will come in with our data and we'll immediately see return and we'll celebrate and we will do all these kind of press releases but the India will lose a competitive advantage on something which is very critical for the next decade."

ANI

18,753 次观看 • 5 个月前

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."
3:06

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Patrick OShaughnessy

393,533 次观看 • 5 个月前

Sam Altman explains why “the great wave” is the most important concept for good startup ideas “This idea of the great wave - I think this is the most important concept to find good ideas out there. People wonder why startups cluster in small periods of time… The reason is that there are these great waves.” He gives the example of the internet giving rise to a number of massive companies in the late 90s and early 2000s. Then smartphones gave rise to another group of companies in the 2009-2011 period. “All of a sudden, new things are possible that were never possible before. And when that happens - because startups can move so quickly - you can do things that otherwise would never happen. Or that a big company would otherwise win on. You want to think about what the next great wave is.” In this lecture that was given in 2017, Sam predicts that the next great wave will be “machine learning applied to every vertical.” He continues: “You want to identify this - What is the big tectonic shift that’s going to happen in the next couple of years? And do something that’s enabled by that and built on that platform.” Video source: Stanford Online (2017)
0:59

Sam Altman explains why “the great wave” is the most important concept for good startup ideas “This idea of the great wave - I think this is the most important concept to find good ideas out there. People wonder why startups cluster in small periods of time… The reason is that there are these great waves.” He gives the example of the internet giving rise to a number of massive companies in the late 90s and early 2000s. Then smartphones gave rise to another group of companies in the 2009-2011 period. “All of a sudden, new things are possible that were never possible before. And when that happens - because startups can move so quickly - you can do things that otherwise would never happen. Or that a big company would otherwise win on. You want to think about what the next great wave is.” In this lecture that was given in 2017, Sam predicts that the next great wave will be “machine learning applied to every vertical.” He continues: “You want to identify this - What is the big tectonic shift that’s going to happen in the next couple of years? And do something that’s enabled by that and built on that platform.” Video source: Stanford Online (2017)

Startup Archive

12,919 次观看 • 5 个月前

Sam Altman explains why “the great wave” is the most important concept for good startup ideas “This idea of the great wave - I think this is the most important concept to find good ideas out there. People wonder why startups cluster in small periods of time… The reason is that there are these great waves.” He gives the example of the internet giving rise to a number of massive companies in the late 90s and early 2000s. Then smartphones gave rise to another group of companies in the 2009-2011 period. “All of a sudden, new things are possible that were never possible before. And when that happens - because startups can move so quickly - you can do things that otherwise would never happen. Or that a big company would otherwise win on. You want to think about what the next great wave is.” In this lecture that was given in 2017, Sam predicts that the next great wave will be “machine learning applied to every vertical.” He continues: “You want to identify this - What is the big tectonic shift that’s going to happen in the next couple of years? And do something that’s enabled by that and built on that platform.” Video source: Stanford Online (2017)
0:59

Sam Altman explains why “the great wave” is the most important concept for good startup ideas “This idea of the great wave - I think this is the most important concept to find good ideas out there. People wonder why startups cluster in small periods of time… The reason is that there are these great waves.” He gives the example of the internet giving rise to a number of massive companies in the late 90s and early 2000s. Then smartphones gave rise to another group of companies in the 2009-2011 period. “All of a sudden, new things are possible that were never possible before. And when that happens - because startups can move so quickly - you can do things that otherwise would never happen. Or that a big company would otherwise win on. You want to think about what the next great wave is.” In this lecture that was given in 2017, Sam predicts that the next great wave will be “machine learning applied to every vertical.” He continues: “You want to identify this - What is the big tectonic shift that’s going to happen in the next couple of years? And do something that’s enabled by that and built on that platform.” Video source: Stanford Online (2017)

Startup Archive

32,091 次观看 • 1 年前

"The first thing [RFK Jr.] will do is...make the data transparent...[it] will show...there's an autism epidemic...engineered with vaccines...[But] how many parents...in America...have aggressively vaccinated their children and don't wanna hear that they poisoned their children?" In this clip from a recent interview with Shannon Joy (Shannon Joy), investment banker, former HUD official, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts) Catherine Austin Fitts describes what she thinks RFK Jr. will do once he is appointed as HHS Secretary in Trump's incoming administration. She notes that he will likely make the data surrounding "vaccines" and autism transparent, but that the truth of the matter may "[tear] the body politic apart." "We have a role, and our role is to tell the truth. That's one role. Okay. Another role is to try to get something done in the machine. Okay? And so that's a different strategy," Fitts says. "And if you look at Bobby's strategies, his strategy has been focused on getting things done in the machine. Now here's my guess as to what he will do first: The first thing he will do is simply go through the bureaucracy and try and get the actual data and make the data transparent. Because what the data will show is there's an autism epidemic because it's being engineered with vaccines. "That's...gonna show," Fitts says. "But the tactic that will be most successful is complete transparency—disgorge that data get the data out, and then have an honest conversation. Now, let me tell you the problem with the honest conversation. How many parents do you have in America who have aggressively vaccinated their children and don't wanna hear that they poisoned their children?" Fitts goes on ot say: "So this is not just pharmaceutical companies. This is a whole world of people who've been actively engaged in poisoning their fellow citizens and their kids, and they don't wanna hear this. So the entropy that you're facing and the tactics you have to use, you know, are a little different...So what will the next steps be? I don't know what the next steps be. Our big fear...is the modified hangout [that] will...affirm the credibility of most vaccines and the autism epidemic will continue. Because, if you look at the world of vaccines, the official narrative and the truth are so far apart. You know, how do you get them back in alignment without tearing the body politic apart?
2:08

"The first thing [RFK Jr.] will do is...make the data transparent...[it] will show...there's an autism epidemic...engineered with vaccines...[But] how many parents...in America...have aggressively vaccinated their children and don't wanna hear that they poisoned their children?" In this clip from a recent interview with Shannon Joy (Shannon Joy), investment banker, former HUD official, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts) Catherine Austin Fitts describes what she thinks RFK Jr. will do once he is appointed as HHS Secretary in Trump's incoming administration. She notes that he will likely make the data surrounding "vaccines" and autism transparent, but that the truth of the matter may "[tear] the body politic apart." "We have a role, and our role is to tell the truth. That's one role. Okay. Another role is to try to get something done in the machine. Okay? And so that's a different strategy," Fitts says. "And if you look at Bobby's strategies, his strategy has been focused on getting things done in the machine. Now here's my guess as to what he will do first: The first thing he will do is simply go through the bureaucracy and try and get the actual data and make the data transparent. Because what the data will show is there's an autism epidemic because it's being engineered with vaccines. "That's...gonna show," Fitts says. "But the tactic that will be most successful is complete transparency—disgorge that data get the data out, and then have an honest conversation. Now, let me tell you the problem with the honest conversation. How many parents do you have in America who have aggressively vaccinated their children and don't wanna hear that they poisoned their children?" Fitts goes on ot say: "So this is not just pharmaceutical companies. This is a whole world of people who've been actively engaged in poisoning their fellow citizens and their kids, and they don't wanna hear this. So the entropy that you're facing and the tactics you have to use, you know, are a little different...So what will the next steps be? I don't know what the next steps be. Our big fear...is the modified hangout [that] will...affirm the credibility of most vaccines and the autism epidemic will continue. Because, if you look at the world of vaccines, the official narrative and the truth are so far apart. You know, how do you get them back in alignment without tearing the body politic apart?

Sense Receptor

31,122 次观看 • 1 年前

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!
1:33

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!

DeepLearning.AI

20,833 次观看 • 1 年前

"The question becomes: when does the crash happen? And then, how long does it last?" "And when you come out of it, what are the new technologies that all the companies have to be built on?" Insight Ventures co-founder Jerry Murdock (jerry murdock) identifies the three major trends that will outlast this cycle, and be the dominant platforms of the next.
1:08

"The question becomes: when does the crash happen? And then, how long does it last?" "And when you come out of it, what are the new technologies that all the companies have to be built on?" Insight Ventures co-founder Jerry Murdock (jerry murdock) identifies the three major trends that will outlast this cycle, and be the dominant platforms of the next.

TBPN

12,324 次观看 • 8 个月前

The difference between a Harvard Business School student and a Founder. HBS STUDENT: Lists all the reasons AI Space Compute is hard. FOUNDER: “ I don't disagree with you that building data centers in space is hard. Building data centers in space is extremely hard. . . If you think [demand] is going to double in the next three years - demand for energy from just data centers - and then double in the next three years after that, and then double in the next three years after that, then: We don’t have a choice. We literally have to put data centers in space. So we have to figure this stuff out.” Philip Johnston Starcloud Founders solve problems. Founders KNOW it will be HARD and do it anyway because it simply NEEDS TO BE DONE. That is founder mindset - and that mindset is what Harvard Business School can’t teach.
0:54

The difference between a Harvard Business School student and a Founder. HBS STUDENT: Lists all the reasons AI Space Compute is hard. FOUNDER: “ I don't disagree with you that building data centers in space is hard. Building data centers in space is extremely hard. . . If you think [demand] is going to double in the next three years - demand for energy from just data centers - and then double in the next three years after that, and then double in the next three years after that, then: We don’t have a choice. We literally have to put data centers in space. So we have to figure this stuff out.” Philip Johnston Starcloud Founders solve problems. Founders KNOW it will be HARD and do it anyway because it simply NEEDS TO BE DONE. That is founder mindset - and that mindset is what Harvard Business School can’t teach.

Amy

19,909 次观看 • 4 个月前

FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: “The internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, there’s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not they’re using it to train, I don’t know. I’ve heard from insiders they’re not allowed to yet and would have to redo the terms of service.” Source: AIFinInsights david friedberg
2:12

FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: “The internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, there’s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not they’re using it to train, I don’t know. I’ve heard from insiders they’re not allowed to yet and would have to redo the terms of service.” Source: AIFinInsights david friedberg

Mario Nawfal

15,855 次观看 • 7 个月前

David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”
1:33

David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”

The All-In Podcast

304,297 次观看 • 7 个月前

Marriane Kitany: This bill, the only thing that it is doing is to make sure that the ISPs, when they are selling their data bundles, do not come and give a figure and say I am going to sell you my data bundle for this much for this particular period knowing very well that you will not consume that amount of data by that particular period. This bill has come to take care of the consumer rights #CitizenMondayReport Trevor Ombija, HSC
4:30

Marriane Kitany: This bill, the only thing that it is doing is to make sure that the ISPs, when they are selling their data bundles, do not come and give a figure and say I am going to sell you my data bundle for this much for this particular period knowing very well that you will not consume that amount of data by that particular period. This bill has come to take care of the consumer rights #CitizenMondayReport Trevor Ombija, HSC

Citizen TV Kenya

69,641 次观看 • 1 年前

I got a smart meter recently and saw you can download a HDF file with the data so I had the idea of writing a script that could parse that and show the data in a useful manner. However I discovered that someone has already done this and done a really good job on it. The video explains how it works. In simple terms it uses the ESB smart meter data and shows a breakdown of how much data you are using and when and also recommends plans and estimates what each one would cost based on the data. The tool is available at easier to read on Laptop or Tablet or then your phone to Landscape.
1:26

I got a smart meter recently and saw you can download a HDF file with the data so I had the idea of writing a script that could parse that and show the data in a useful manner. However I discovered that someone has already done this and done a really good job on it. The video explains how it works. In simple terms it uses the ESB smart meter data and shows a breakdown of how much data you are using and when and also recommends plans and estimates what each one would cost based on the data. The tool is available at easier to read on Laptop or Tablet or then your phone to Landscape.

Carlow Weather

298,521 次观看 • 2 年前

The data center boom has a geopolitical shadow. Think about it- in a world where military systems run on compute, the buildings that provide that compute will become targets. And the messy part is that a lot of this infrastructure is dual-use with civilian data, banking data, cloud services, defense contracts, intelligence workloads all potentially blurred inside the same ecosystem. So if these facilities are being built next to neighborhoods, schools, and homes, people deserve to know more than “it brings jobs.” They deserve to know what kind of infrastructure they’re living next to, what risks come with it, and whether their community is secretly being folded into the map of modern warfare.
1:53

The data center boom has a geopolitical shadow. Think about it- in a world where military systems run on compute, the buildings that provide that compute will become targets. And the messy part is that a lot of this infrastructure is dual-use with civilian data, banking data, cloud services, defense contracts, intelligence workloads all potentially blurred inside the same ecosystem. So if these facilities are being built next to neighborhoods, schools, and homes, people deserve to know more than “it brings jobs.” They deserve to know what kind of infrastructure they’re living next to, what risks come with it, and whether their community is secretly being folded into the map of modern warfare.

Drey

13,996 次观看 • 1 个月前

Eric Schmidt, former Google CEO, on why top programmers won't be replaced by AI, they'll be amplified by it: He starts with an observation about the existing hierarchy in software: "The very top programmers were worth 10 times more than the ones right below. There's something special about the mathematical reasoning skills of programmers." Rather than flattening that gap, he argues AI will widen it: "Those people will become more valuable, not less valuable, because these systems need to be controlled by humans at the moment. Those people will be capable of grasping the parallelization and the activities of this." To show what this amplification actually looks like in practice, Eric shares a story from a startup he's involved with. He was talking to one of the programmers there, who works on UIs, about his daily workflow: "He said, 'I write the spec of what I want and then I write a test function, an evaluation function. And then I turn it on.' I said, 'What time?' And he goes, '7:00 in the evening.' And I go, 'Okay. What do you then do?' Well, he has dinner with his wife and he goes to sleep." Eric continues: "I said, 'Do you wake up?' Said, 'No, I sleep very well.' 'When does it finish?' 'Oh, 4:00 in the morning.' And then he gets up, has breakfast, you know, does whatever he does, and then he sees what's been good." Eric Schmidt calls the whole thing "mindboggling." The story captures what amplification really means. The programmer isn't writing less code. He's producing a night's worth of work while asleep, because the machine is running on his spec, his tests, his judgment of what "good" looks like. The leverage belongs to those who can define the problem precisely, write the tests that matter, and recognize good output when they see it.
1:02

Eric Schmidt, former Google CEO, on why top programmers won't be replaced by AI, they'll be amplified by it: He starts with an observation about the existing hierarchy in software: "The very top programmers were worth 10 times more than the ones right below. There's something special about the mathematical reasoning skills of programmers." Rather than flattening that gap, he argues AI will widen it: "Those people will become more valuable, not less valuable, because these systems need to be controlled by humans at the moment. Those people will be capable of grasping the parallelization and the activities of this." To show what this amplification actually looks like in practice, Eric shares a story from a startup he's involved with. He was talking to one of the programmers there, who works on UIs, about his daily workflow: "He said, 'I write the spec of what I want and then I write a test function, an evaluation function. And then I turn it on.' I said, 'What time?' And he goes, '7:00 in the evening.' And I go, 'Okay. What do you then do?' Well, he has dinner with his wife and he goes to sleep." Eric continues: "I said, 'Do you wake up?' Said, 'No, I sleep very well.' 'When does it finish?' 'Oh, 4:00 in the morning.' And then he gets up, has breakfast, you know, does whatever he does, and then he sees what's been good." Eric Schmidt calls the whole thing "mindboggling." The story captures what amplification really means. The programmer isn't writing less code. He's producing a night's worth of work while asleep, because the machine is running on his spec, his tests, his judgment of what "good" looks like. The leverage belongs to those who can define the problem precisely, write the tests that matter, and recognize good output when they see it.

Big Brain AI

15,319 次观看 • 2 个月前

So here’s the deal, THE TREASURY DEPARTMENT DATA IS COMPROMISED. Actors without security clearance have been given access to scrape your data. Elon Musk now controls your data and it represents the same level of control exerted by the Chinese government’s social credit system. Musk is now able to profile every name on the treasury data systems. There is no way to sugar coat the fact that your data is no longer secure. It can now be used to target you and not for your benefit, be sure of that. Democrats and advocacy groups can take all the legal action they like, THE DATA IS COMPROMISED and you can’t put the genie back in the bottle. This data can now be used to literally social engineer services to target any perceived enemy of ‘the state’. Call me a conspiracy theorist if you like, but then sit back have a think if any of what I am suggesting is not possible. I analyse big data for a living and I have no solution to this problem and neither does any Democrat telling you it can be fixed.
2:09

So here’s the deal, THE TREASURY DEPARTMENT DATA IS COMPROMISED. Actors without security clearance have been given access to scrape your data. Elon Musk now controls your data and it represents the same level of control exerted by the Chinese government’s social credit system. Musk is now able to profile every name on the treasury data systems. There is no way to sugar coat the fact that your data is no longer secure. It can now be used to target you and not for your benefit, be sure of that. Democrats and advocacy groups can take all the legal action they like, THE DATA IS COMPROMISED and you can’t put the genie back in the bottle. This data can now be used to literally social engineer services to target any perceived enemy of ‘the state’. Call me a conspiracy theorist if you like, but then sit back have a think if any of what I am suggesting is not possible. I analyse big data for a living and I have no solution to this problem and neither does any Democrat telling you it can be fixed.

𝔗𝔯𝔲𝔱𝔥 𝔐𝔞𝔱𝔱𝔢𝔯𝔰

16,366 次观看 • 1 年前