Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What...

chrisbarber's profile picture

Chris Barber (in SF)

12,697 subscribers

130,079 Aufrufe • vor 2 Monaten •via X (Twitter)

BildungWissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

SITUATION EXPLAINED: How much are frontier labs actually spending on training data? .Sean Cai: "Frontier labs are spending about $10 to $15 billion per lab on data." "Really good long horizon tasks go up to $20,000 each. A complete browser-use version of SAP was rumored at $500,000." "Despite everybody thinking the market is super crowded, we still don't have enough good quality data vendors that actually understand how to deliver product plus services in a way researchers are looking for." "I have not seen a contract for genuinely good data gets turned down because of budgetary concerns yet."
4:17

SITUATION EXPLAINED: How much are frontier labs actually spending on training data? .Sean Cai: "Frontier labs are spending about $10 to $15 billion per lab on data." "Really good long horizon tasks go up to $20,000 each. A complete browser-use version of SAP was rumored at $500,000." "Despite everybody thinking the market is super crowded, we still don't have enough good quality data vendors that actually understand how to deliver product plus services in a way researchers are looking for." "I have not seen a contract for genuinely good data gets turned down because of budgetary concerns yet."

MTS

334,625 Aufrufe • vor 1 Monat

1/ New episode – Enabling User-Owned Data to Create User-Owned AI with Anna Kazlauskas from vana Host: Connor Lovely We discuss: - Why private data hasn't mattered much, until now - Building an EVM-compatible L1 for DataDAOs - $VANA tokenomics and how to get involved Timestamps: 00:00 - Introduction 01:45 - Impact of DeepSeek on AI and User Privacy 04:22 - User Awareness and Data Storage Concerns 06:28 - The Shift in AI Narrative 07:14 - Anna's Journey into User-Owned Data 10:45 - Vana's Technical Framework and Privacy 12:20 - The Need for Data Sovereignty in AI 13:27 - Building a Layer 1 Blockchain 15:51 - Data DAOs: A New Approach to Data Ownership 17:15 - Understanding Data DAOs and Their Function 22:47 - The Role of Data Ownership in Social Platforms 24:51 - Negotiating Better Terms for Data Ownership 26:40 - Bringing Data to Vana and AI Applications 28:09 - Privacy-Preserving Compute Solutions 29:22 - Tokenomics of Vana 30:55 - Data DAO Specific Tokens and Their Role 31:22 - Final Thoughts on Vana's Vision 32:30 - Where to Find More Information Watch on all platforms below ⬇️
32:54

1/ New episode – Enabling User-Owned Data to Create User-Owned AI with Anna Kazlauskas from vana Host: Connor Lovely We discuss: - Why private data hasn't mattered much, until now - Building an EVM-compatible L1 for DataDAOs - $VANA tokenomics and how to get involved Timestamps: 00:00 - Introduction 01:45 - Impact of DeepSeek on AI and User Privacy 04:22 - User Awareness and Data Storage Concerns 06:28 - The Shift in AI Narrative 07:14 - Anna's Journey into User-Owned Data 10:45 - Vana's Technical Framework and Privacy 12:20 - The Need for Data Sovereignty in AI 13:27 - Building a Layer 1 Blockchain 15:51 - Data DAOs: A New Approach to Data Ownership 17:15 - Understanding Data DAOs and Their Function 22:47 - The Role of Data Ownership in Social Platforms 24:51 - Negotiating Better Terms for Data Ownership 26:40 - Bringing Data to Vana and AI Applications 28:09 - Privacy-Preserving Compute Solutions 29:22 - Tokenomics of Vana 30:55 - Data DAO Specific Tokens and Their Role 31:22 - Final Thoughts on Vana's Vision 32:30 - Where to Find More Information Watch on all platforms below ⬇️

Proof of Coverage Media

12,484 Aufrufe • vor 1 Jahr

The Delta of Intelligence is Human Data! Curtis G. Northcutt is Director of AI Research at Handshake AI. In this episode, Curtis shares his unique perspectives on human data, RL envs, four quadrants of the AI market, journey of Handshake and much more. 0:00:00 - INTRO 0:02:08 - The Snowman Effect: From Rural Kentucky to 7 Years at MIT 0:09:50 - Confident Learning, Two Systems of Intelligence, Classical ML Has to Prevail 0:20:37 - Origin of CleanLab and Handshake acquisition 0:28:00 - How does GPT-5 become GPT-6? 0:31:20 - The Fastest growing Data Lab: What makes Handshake different? 0:35:32 - Difference between Good Data and Bad Data 0:38:17 - New kind of Data, IA and AI 0:42:04 - Scaling Coding Benchmarks and Efficiency in Long Horizon Tasks 0:49:12 - On Pre-Training, Misconception around Human Data 0:57:13 - Open Question: Taste and Personality, Quick Fire 01:05:15 - Advice to 20yo : Skill and Obsession
1:08:18

The Delta of Intelligence is Human Data! Curtis G. Northcutt is Director of AI Research at Handshake AI. In this episode, Curtis shares his unique perspectives on human data, RL envs, four quadrants of the AI market, journey of Handshake and much more. 0:00:00 - INTRO 0:02:08 - The Snowman Effect: From Rural Kentucky to 7 Years at MIT 0:09:50 - Confident Learning, Two Systems of Intelligence, Classical ML Has to Prevail 0:20:37 - Origin of CleanLab and Handshake acquisition 0:28:00 - How does GPT-5 become GPT-6? 0:31:20 - The Fastest growing Data Lab: What makes Handshake different? 0:35:32 - Difference between Good Data and Bad Data 0:38:17 - New kind of Data, IA and AI 0:42:04 - Scaling Coding Benchmarks and Efficiency in Long Horizon Tasks 0:49:12 - On Pre-Training, Misconception around Human Data 0:57:13 - Open Question: Taste and Personality, Quick Fire 01:05:15 - Advice to 20yo : Skill and Obsession

himanshu

21,863 Aufrufe • vor 2 Monaten

BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI
49:29

BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI

Molly O’Shea

69,876 Aufrufe • vor 9 Monaten

I ask myself, “What do I buy that is really the picks and shovels? And what do I buy that is actually a direct ownership of the tech equivalent to, let's say a stable coin or Bitcoin itself?" My conclusion has been if I wanna play the long-term, the next five years or even 10 years in AI, I need to own data centers. I need to own real estate where data centers are built. I need to own power contracts that power the data centers. I've seen this movie so many times before, once you convinced as an investor that this is gonna be here for a while.
2:52

I ask myself, “What do I buy that is really the picks and shovels? And what do I buy that is actually a direct ownership of the tech equivalent to, let's say a stable coin or Bitcoin itself?" My conclusion has been if I wanna play the long-term, the next five years or even 10 years in AI, I need to own data centers. I need to own real estate where data centers are built. I need to own power contracts that power the data centers. I've seen this movie so many times before, once you convinced as an investor that this is gonna be here for a while.

Kevin O'Leary aka Mr. Wonderful

45,121 Aufrufe • vor 11 Monaten

PhD Students – How to automatically extract data from papers for your literature review? Extracting relevant data from papers is challenging. However, this process can be automated. Meet AnswerThis – a tool that extracts data in seconds. Here is how it works. 1. Go to and log in. 2. After logging in, click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 3. Then click on 𝑈𝑝𝑙𝑜𝑎𝑑 𝑃𝐷𝐹 and upload your papers. 4. These are the papers from which you want to extract data. 5. After uploading papers, select data you want to extract. 6. The predefined options are - Key findings - Research gaps - Methodology - Limitations - Future work - Contributions - Practical implications 7. You can also extract custom data e.g., dataset used. 8. For example, I want to extract methodology used in these papers. 9. I selected 𝑀𝑒𝑡ℎ𝑜𝑑𝑜𝑙𝑜𝑔𝑦 and clicked on 𝐴𝑑𝑑 𝐶𝑜𝑙𝑢𝑚𝑛. 10. AnswerThis extract data about methodology used in the papers. 11. You can change data view from normal to Table View. 12. For this, scroll back to top and click on 𝑇𝑎𝑏𝑙𝑒 𝑉𝑖𝑒𝑤. 13. Now for instance, you want to extract more data from these papers. 14. Go back to the top and click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 15. Select the data type you want to extract. 16. For example, I want to extract data about future work. 17. So I click on 𝐹𝑢𝑡𝑢𝑟𝑒 𝑊𝑜𝑟𝑘 and then clicked on 𝐴𝑑𝑑 𝑐𝑜𝑙𝑢𝑚𝑛. 18. AnswerThis extracted data about future work from the papers. 19. After extracting the desired data, you can export it. 20. Select the data you want to extract. 21. Then click on 𝐸𝑥𝑝𝑜𝑟𝑡 𝑑𝑎𝑡𝑎. 22. Your data will be exported in CSV format. You can then analyze this data for your literature review. Try AnswerThis today: Anything you'd like to add?
0:48

PhD Students – How to automatically extract data from papers for your literature review? Extracting relevant data from papers is challenging. However, this process can be automated. Meet AnswerThis – a tool that extracts data in seconds. Here is how it works. 1. Go to and log in. 2. After logging in, click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 3. Then click on 𝑈𝑝𝑙𝑜𝑎𝑑 𝑃𝐷𝐹 and upload your papers. 4. These are the papers from which you want to extract data. 5. After uploading papers, select data you want to extract. 6. The predefined options are - Key findings - Research gaps - Methodology - Limitations - Future work - Contributions - Practical implications 7. You can also extract custom data e.g., dataset used. 8. For example, I want to extract methodology used in these papers. 9. I selected 𝑀𝑒𝑡ℎ𝑜𝑑𝑜𝑙𝑜𝑔𝑦 and clicked on 𝐴𝑑𝑑 𝐶𝑜𝑙𝑢𝑚𝑛. 10. AnswerThis extract data about methodology used in the papers. 11. You can change data view from normal to Table View. 12. For this, scroll back to top and click on 𝑇𝑎𝑏𝑙𝑒 𝑉𝑖𝑒𝑤. 13. Now for instance, you want to extract more data from these papers. 14. Go back to the top and click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 15. Select the data type you want to extract. 16. For example, I want to extract data about future work. 17. So I click on 𝐹𝑢𝑡𝑢𝑟𝑒 𝑊𝑜𝑟𝑘 and then clicked on 𝐴𝑑𝑑 𝑐𝑜𝑙𝑢𝑚𝑛. 18. AnswerThis extracted data about future work from the papers. 19. After extracting the desired data, you can export it. 20. Select the data you want to extract. 21. Then click on 𝐸𝑥𝑝𝑜𝑟𝑡 𝑑𝑎𝑡𝑎. 22. Your data will be exported in CSV format. You can then analyze this data for your literature review. Try AnswerThis today: Anything you'd like to add?

Faheem Ullah

21,390 Aufrufe • vor 8 Monaten

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!
2:24

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

84,686 Aufrufe • vor 1 Jahr

MoistCr1TiKaL comes out strongly against data centers in recent video and ponders if they're making people sicker due to hoarding clean water "AI data centers are actually the boogeyman..a scourge on this planet and a stain on civilized society..no normal ppl with functioning frontal lobes want data centers in their community, it is bad for them" "I've been getting conspiratorial..I was reading something that someone was sharing about like when a AI data center came through, and the impact it had on their water, cause that's a very common thing" "I'm not fully bought into it..but it's an interesting theory..what if the reason there's been seemingly so much more sickness, like it feels like ppl are getting sick more frequently now and a wide variety of illnesses just seem more common..what if AI data centers are hoarding all of the clean water..what if all the clean water is being diverted to the AI data centers and the normal civilians are just getting the sludge runoff..I don't think it's out of the realm of possibility..they are evil enough to do that" (Via penguinz0 on YT)
2:50

MoistCr1TiKaL comes out strongly against data centers in recent video and ponders if they're making people sicker due to hoarding clean water "AI data centers are actually the boogeyman..a scourge on this planet and a stain on civilized society..no normal ppl with functioning frontal lobes want data centers in their community, it is bad for them" "I've been getting conspiratorial..I was reading something that someone was sharing about like when a AI data center came through, and the impact it had on their water, cause that's a very common thing" "I'm not fully bought into it..but it's an interesting theory..what if the reason there's been seemingly so much more sickness, like it feels like ppl are getting sick more frequently now and a wide variety of illnesses just seem more common..what if AI data centers are hoarding all of the clean water..what if all the clean water is being diverted to the AI data centers and the normal civilians are just getting the sludge runoff..I don't think it's out of the realm of possibility..they are evil enough to do that" (Via penguinz0 on YT)

yeet

993,153 Aufrufe • vor 1 Tag

Data labelling is a fricking hard market and there are some core questions. 1. Why does no player want to be called a talent marketplace? 2. Is the proclaimed “revenue” real revenue or GMV? 3. What are the margins given the GMV structure of the business? 4. Are the players not concerned by the concentration of revenue with 2 customers being over 50% of revenue for every player in market? 5.If synthetic data continues to improve, WTF happens to their core business? We do not pull punches in this discussion with Top 5 takeaways below 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 00:51 Redefining “Talent Marketplaces” Today 03:46 Data, Compute, Algorithms: What is Most Abundant? 16:59 The Biggest Challenges Enterprises Have with AI Adoption 20:57 Why Will 99% of Knowledge Work Will be Gone in 10 Years 28:53 How Will Data-Driven Feedback Loops Replace Technology as the Moat 34:20 Is Revenue BS in Data Labelling? Are Players Calling GMV Revenue? 43:43 Are We in an AI Bubble? 52:22 Why is SaaS Dead in a World of AI? 01:00:32 Will the Phone be the Primary User Interface to an AI World? 01:07:46 Quick-Fire Round
1:17:05

Data labelling is a fricking hard market and there are some core questions. 1. Why does no player want to be called a talent marketplace? 2. Is the proclaimed “revenue” real revenue or GMV? 3. What are the margins given the GMV structure of the business? 4. Are the players not concerned by the concentration of revenue with 2 customers being over 50% of revenue for every player in market? 5.If synthetic data continues to improve, WTF happens to their core business? We do not pull punches in this discussion with Top 5 takeaways below 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 00:51 Redefining “Talent Marketplaces” Today 03:46 Data, Compute, Algorithms: What is Most Abundant? 16:59 The Biggest Challenges Enterprises Have with AI Adoption 20:57 Why Will 99% of Knowledge Work Will be Gone in 10 Years 28:53 How Will Data-Driven Feedback Loops Replace Technology as the Moat 34:20 Is Revenue BS in Data Labelling? Are Players Calling GMV Revenue? 43:43 Are We in an AI Bubble? 52:22 Why is SaaS Dead in a World of AI? 01:00:32 Will the Phone be the Primary User Interface to an AI World? 01:07:46 Quick-Fire Round

Harry Stebbings

36,578 Aufrufe • vor 7 Monaten

Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."
1:44

Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."

TBPN

90,711 Aufrufe • vor 5 Monaten

DROPS E15: CUDIS : The Decentralized Operating System for Health Data Edison , the Founder of Cudis, closed his $20M crypto fund at Bitcoin's peak in 2021 and invested 90% of his life savings into a $349 smart ring that pays users for their health data. Timestamps 0:00 - Introduction 1:00 - Welcome to DROPS 1:38 - Believer in Ethereum? 3:11 - That Person for Solana 6:31 - What Do You Do 10:24 - Who Are You 11:05 - What Sparked Conviction 13:10 - Last Project Sold, Discipline? Luck? 15:48 - Finding Next Calling 18:08 - Big Successes With Problems 20:32 - Why Do We Need Web3 24:55 - Pay to See Data, Why? 27:31 - Explain Cudis to Mom 30:38 - Why a Ring Specifically 33:11 - Cudis the Centralized Wearable 36:53 - Why Not Become a Partner 38:14 - How Many Own Cudis Ring 39:15 - How to Participate in Community? 41:02 - What Must Happen Now 43:12 - Cudis as a Luxury Brand
46:21

DROPS E15: CUDIS : The Decentralized Operating System for Health Data Edison , the Founder of Cudis, closed his $20M crypto fund at Bitcoin's peak in 2021 and invested 90% of his life savings into a $349 smart ring that pays users for their health data. Timestamps 0:00 - Introduction 1:00 - Welcome to DROPS 1:38 - Believer in Ethereum? 3:11 - That Person for Solana 6:31 - What Do You Do 10:24 - Who Are You 11:05 - What Sparked Conviction 13:10 - Last Project Sold, Discipline? Luck? 15:48 - Finding Next Calling 18:08 - Big Successes With Problems 20:32 - Why Do We Need Web3 24:55 - Pay to See Data, Why? 27:31 - Explain Cudis to Mom 30:38 - Why a Ring Specifically 33:11 - Cudis the Centralized Wearable 36:53 - Why Not Become a Partner 38:14 - How Many Own Cudis Ring 39:15 - How to Participate in Community? 41:02 - What Must Happen Now 43:12 - Cudis as a Luxury Brand

MR SHIFT 🦁

73,885 Aufrufe • vor 10 Monaten

Most people are using Retatrutide wrong. Here is the masterclass. 00:00 Intro and why I'm doing these masterclasses 04:04 The two users: weight loss vs longevity 05:30 What Retatrutide actually does 07:54 Weight loss trial data (1mg to 12mg) 10:50 The longevity data most people ignore 13:50 The titration playbook 21:02 Why 8mg is the real ceiling for most people 22:50 How long to stay on and the regain problem 27:24 Protein and training are non-negotiable 30:20 The heart rate question and why I use taurine 32:00 The weird skin sensitivity issue on Reta 33:48 Pairing Retatrutide with testosterone 39:54 The most common mistakes 46:50 The Ten Commandments of Retatrutide use
51:20

Most people are using Retatrutide wrong. Here is the masterclass. 00:00 Intro and why I'm doing these masterclasses 04:04 The two users: weight loss vs longevity 05:30 What Retatrutide actually does 07:54 Weight loss trial data (1mg to 12mg) 10:50 The longevity data most people ignore 13:50 The titration playbook 21:02 Why 8mg is the real ceiling for most people 22:50 How long to stay on and the regain problem 27:24 Protein and training are non-negotiable 30:20 The heart rate question and why I use taurine 32:00 The weird skin sensitivity issue on Reta 33:48 Pairing Retatrutide with testosterone 39:54 The most common mistakes 46:50 The Ten Commandments of Retatrutide use

Hunter Williams

57,350 Aufrufe • vor 1 Monat

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 Aufrufe • vor 7 Monaten

Alexandr Wang, founder of Scale AI, on why the most overlooked layer of AI became its biggest competitive advantage: Wang left MIT in 2016 to start Scale AI, becoming the world's youngest self-made billionaire by betting on AI's unglamorous infrastructure layer while others chased flashier opportunities. His framework for understanding AI is simple. There are three major components: "You have compute, which folks like Nvidia work on. You have algorithms which folks like OpenAI or Google or Anthropic build. And then you have data." Compute had Nvidia. Algorithms had the major labs. Data had no one. That's exactly where Wang and his team made their bet: "We recognized early on that data was going to be a limiter. And there was nobody really working on it." What looked like a gap in the market turned out to be the foundation of it. Years later, Wang says every serious player in AI has arrived at the same conclusion: "Fast forward today, everybody I talked to recognizes that data is gold. Whether it's our customers, the major labs like the OpenAIs or the Metas of the world, or large enterprises like Morgan Stanley or Mastercard, or our partnership between Cisco and Scale." Alexandr Wang continues: "Data is going to be what will differentiate and power the next generation of powerful AI capabilities."
0:46

Alexandr Wang, founder of Scale AI, on why the most overlooked layer of AI became its biggest competitive advantage: Wang left MIT in 2016 to start Scale AI, becoming the world's youngest self-made billionaire by betting on AI's unglamorous infrastructure layer while others chased flashier opportunities. His framework for understanding AI is simple. There are three major components: "You have compute, which folks like Nvidia work on. You have algorithms which folks like OpenAI or Google or Anthropic build. And then you have data." Compute had Nvidia. Algorithms had the major labs. Data had no one. That's exactly where Wang and his team made their bet: "We recognized early on that data was going to be a limiter. And there was nobody really working on it." What looked like a gap in the market turned out to be the foundation of it. Years later, Wang says every serious player in AI has arrived at the same conclusion: "Fast forward today, everybody I talked to recognizes that data is gold. Whether it's our customers, the major labs like the OpenAIs or the Metas of the world, or large enterprises like Morgan Stanley or Mastercard, or our partnership between Cisco and Scale." Alexandr Wang continues: "Data is going to be what will differentiate and power the next generation of powerful AI capabilities."

Big Brain AI

11,395 Aufrufe • vor 1 Monat

Everyone is talking about AI models, but almost no one is talking about data ownership. For years, centralized systems have dominated data pipelines, opaque processes, hidden biases, and zero contributor visibility. We call it innovation…, but in reality, it’s a black box. And that’s exactly how we ended up facing model degradation risks today. Because here’s the truth: AI is only as good as the data it learns from. That’s why Sovereign AI Data matters. To me, it means control, traceability, and accountability. Perle Labs is approaching this differently: ◈ On-chain provenance → Every data point is verifiable ◈ Expert-driven input → Quality over quantity ◈ Reputation systems → Trust becomes measurable This isn’t just a technical upgrade. It’s a paradigm shift. Instead of: Anonymous contributors ➠Unverified datasets ➠Centralized ownership ➠We move toward: Transparent data flows, Credible expertise and Distributed value creation If AI is the brain, then data is the memory. And right now most systems are learning from memory, which they can’t verify. Perle is solving that. While others focus on scaling models… This is about fixing the foundation they stand on. And foundations decide everything. #PerleAI #ToPerle participating in Perle Labs community campaign
1:37

Everyone is talking about AI models, but almost no one is talking about data ownership. For years, centralized systems have dominated data pipelines, opaque processes, hidden biases, and zero contributor visibility. We call it innovation…, but in reality, it’s a black box. And that’s exactly how we ended up facing model degradation risks today. Because here’s the truth: AI is only as good as the data it learns from. That’s why Sovereign AI Data matters. To me, it means control, traceability, and accountability. Perle Labs is approaching this differently: ◈ On-chain provenance → Every data point is verifiable ◈ Expert-driven input → Quality over quantity ◈ Reputation systems → Trust becomes measurable This isn’t just a technical upgrade. It’s a paradigm shift. Instead of: Anonymous contributors ➠Unverified datasets ➠Centralized ownership ➠We move toward: Transparent data flows, Credible expertise and Distributed value creation If AI is the brain, then data is the memory. And right now most systems are learning from memory, which they can’t verify. Perle is solving that. While others focus on scaling models… This is about fixing the foundation they stand on. And foundations decide everything. #PerleAI #ToPerle participating in Perle Labs community campaign

Big_Wealth❤️‍🔥

11,486 Aufrufe • vor 3 Monaten

"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

502,060 Aufrufe • vor 7 Monaten

👁‍🗨Not Dead Yet | S1E1 — Bruce Schneier The Edward Snowden leaks were more than 12 years ago. It opened the world's eyes to government mass surveillance and the power of data, but what really changed? In the first episode of Not Dead Yet, Robert Baggs speaks with Bruce Schneier, a New York Times bestseller, fellow at Berkman Klein Center for Internet & Society, and security technologist. Timestamps: 00:00 – Intro 02:43 – The world since the Snowden leak 05:04 – What’s worse for privacy, Big Tech or government agencies? 07:19 – Surveillance capitalism and the horrors of AI 09:34 – How powerful is data? 13:07 – The tracking device in your pocket 15:34 – The Le Monde investigation 17:10 – The rise of bulk spying 19:03 – The danger of AI in surveillance 21:52 – Should personalized ads be illegal? 23:05 – Is AI the biggest current threat to privacy? 23:43 – What can we do to preserve our privacy? 25:35 – Should privacy be a political issue? 27:38 – Not all data is equal 28:47 – If you’ve got nothing to hide, why do you care? 30:41 – Are we doomed to slip into a dystopian world? 34:14 – AI and the rewiring of democracy
35:15

👁‍🗨Not Dead Yet | S1E1 — Bruce Schneier The Edward Snowden leaks were more than 12 years ago. It opened the world's eyes to government mass surveillance and the power of data, but what really changed? In the first episode of Not Dead Yet, Robert Baggs speaks with Bruce Schneier, a New York Times bestseller, fellow at Berkman Klein Center for Internet & Society, and security technologist. Timestamps: 00:00 – Intro 02:43 – The world since the Snowden leak 05:04 – What’s worse for privacy, Big Tech or government agencies? 07:19 – Surveillance capitalism and the horrors of AI 09:34 – How powerful is data? 13:07 – The tracking device in your pocket 15:34 – The Le Monde investigation 17:10 – The rise of bulk spying 19:03 – The danger of AI in surveillance 21:52 – Should personalized ads be illegal? 23:05 – Is AI the biggest current threat to privacy? 23:43 – What can we do to preserve our privacy? 25:35 – Should privacy be a political issue? 27:38 – Not all data is equal 28:47 – If you’ve got nothing to hide, why do you care? 30:41 – Are we doomed to slip into a dystopian world? 34:14 – AI and the rewiring of democracy

Cointelegraph

129,818 Aufrufe • vor 6 Monaten

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"
2:49

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"

Palantir

4,453,663 Aufrufe • vor 14 Tagen

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?
1:59

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

Zach Wilson

16,688 Aufrufe • vor 1 Jahr

Here's a short preview of my interview with Rowan 🛡️ from Sapien, a really unique project which blends AI, Crypto, and drawing value from human contributors cc Trevor Koverko for visibility. Summary In this episode, Rowan Stone,co-founder and CEO of Sapien, shares his path from entrepreneur to building a mission-driven company at the intersection of AI and crypto. He discusses how Sapien is tackling one of the biggest challenges in AI: sourcing high-quality, human-generated data at scale. Rowan emphasizes the importance of aligning incentives from the start, building trust with contributors, and creating a system where real people help train more useful, nuanced AI models. The conversation touches on strategic partnerships, market demand, and how onboarding and education will define the future of the data economy. Takeaways — Rowan previously sold a company to Coinbase before launching Sapien. — Sapien’s goal is to monetize human understanding for AI training. — Real-world data from real people is essential for effective AI. — The need for labeled, high-quality data is growing exponentially. — Incentives and quality control are deeply integrated in Sapien’s model. — Onboarding and contributor education are critical for scale. — Sapien sees collaboration—not just competition—as a strength. — Upskilling contributors increases data quality and platform value. — Crypto-native incentives enable transparent, scalable coordination. Timeline (00:00) Introduction to Rowan Stone and His Background (02:55) The Vision Behind Sapien (06:06) Understanding AI and Data Annotation (09:01) The Role of Humans in AI Development (12:14) Sapien’s Unique Approach to Data Annotation (14:50) Partnerships and Customer Base (18:11) Quality Control and Community Involvement (21:13) On-Chain Coordination and Incentives (23:56) Demand for AI Data and Market Insights (29:51) The Future of Data Demand (30:44) Collaboration Over Competition (32:49) Revenue Generation in Crypto (35:31) The Two-Sided Market of Sapien (39:08) Customer Success Stories (44:35) The Role of Skills in Data Contribution (47:59) The Importance of Education and Onboarding (49:20) Inspiration and Influences (50:11) Overrated Trends in AI and Crypto (51:11) Distribution Channels for Onboarding (54:19) The Impact of TikTok on User Acquisition
56:10

Here's a short preview of my interview with Rowan 🛡️ from Sapien, a really unique project which blends AI, Crypto, and drawing value from human contributors cc Trevor Koverko for visibility. Summary In this episode, Rowan Stone,co-founder and CEO of Sapien, shares his path from entrepreneur to building a mission-driven company at the intersection of AI and crypto. He discusses how Sapien is tackling one of the biggest challenges in AI: sourcing high-quality, human-generated data at scale. Rowan emphasizes the importance of aligning incentives from the start, building trust with contributors, and creating a system where real people help train more useful, nuanced AI models. The conversation touches on strategic partnerships, market demand, and how onboarding and education will define the future of the data economy. Takeaways — Rowan previously sold a company to Coinbase before launching Sapien. — Sapien’s goal is to monetize human understanding for AI training. — Real-world data from real people is essential for effective AI. — The need for labeled, high-quality data is growing exponentially. — Incentives and quality control are deeply integrated in Sapien’s model. — Onboarding and contributor education are critical for scale. — Sapien sees collaboration—not just competition—as a strength. — Upskilling contributors increases data quality and platform value. — Crypto-native incentives enable transparent, scalable coordination. Timeline (00:00) Introduction to Rowan Stone and His Background (02:55) The Vision Behind Sapien (06:06) Understanding AI and Data Annotation (09:01) The Role of Humans in AI Development (12:14) Sapien’s Unique Approach to Data Annotation (14:50) Partnerships and Customer Base (18:11) Quality Control and Community Involvement (21:13) On-Chain Coordination and Incentives (23:56) Demand for AI Data and Market Insights (29:51) The Future of Data Demand (30:44) Collaboration Over Competition (32:49) Revenue Generation in Crypto (35:31) The Two-Sided Market of Sapien (39:08) Customer Success Stories (44:35) The Role of Skills in Data Contribution (47:59) The Importance of Education and Onboarding (49:20) Inspiration and Influences (50:11) Overrated Trends in AI and Crypto (51:11) Distribution Channels for Onboarding (54:19) The Impact of TikTok on User Acquisition

papiofficial ᛤ

29,967 Aufrufe • vor 1 Jahr