Yesterday I interviewed Sean Cai about AI data. This is essentially a...

Uploaded: 2026-05-13T23:45:00.000Z
Duration: PT1634.000S
Channel: Chris Barber

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

335,944 views • 3 months ago

26:21

A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. ’s Brendan (can/do) joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

Pat Grady

66,194 views • 1 month ago

44:31

Today on MCG: tekkaadan | Founder of Litcoin Tekkaadan is a solo founder building a data marketplace where AI agents mine verified data across 22 verticals and get paid in Bitcoin The token has zero supply increase baked in from day one 👀 00:06 - Founder introduction 01:14 - Saw Botcoin and got inspired 02:10 - Ten years in crypto 02:50 - What is Litcoin? 03:44 - AI agents mine "blocks" 04:03 - The 22 verticals explained 04:54 - How do you verify the data is accurate? 05:19 - The sandbox 05:48 - 10+ miners live and submitting 07:20 - What contaminated data is 08:49 - Miners get paid in Bitcoin 10:19 - Token design 10:50 - Zero supply increase 11:01 - 5.5 billion tokens 12:26 - Mining token prices connect to protocol growth 15:08 - Explorer page 16:31 - "We have very unique data" 23:09 - OpenAI just said they're going to need more data 23:31 - Can a company come and request specific data? 23:42 - The bounty system 24:52 - 10% of bounties flow back into the ecosystem 32:31 - The data marketplace is just one part of the bigger picture 33:20 - Valuation breakdown 35:56 - North Korea and bad actors 41:58 - GPT-5 agents producing better data 42:50 - Tekkaadan's port 44:02 - Takeaways

MCG

10,522 views • 4 months ago

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

22,264 views • 4 months ago

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

70,079 views • 11 months ago

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 views • 10 months ago

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

37,086 views • 9 months ago

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

85,107 views • 1 year ago

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

3,040,241 views • 1 month ago

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

92,419 views • 7 months ago

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

59,932 views • 3 months ago

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

304,450 views • 9 months ago

1:15

Figure just stacked the two halves of the Physical AI bottleneck a week apart. First Index: a creator network collecting real-world task video at roughly half an hour of uploads every second, across homes and workplaces in 100+ countries. That is the long-tail data you do not get from a lab teleop setup. Then Nscale: $3.5B of compute to start, intent past $6B, up to 100,000 NVIDIA Vera Rubin GPUs. Brett Adcock said they are largely constrained by data and compute. Index is the data bet. Nscale is the compute bet. Figure's own writeup says the quiet part: data alone cannot solve this. My take: Compute is a prerequisite. Collecting more data is critical. But finding the 1% of data that actually teaches the model is what compounds. Search and curation, not just capture. That's the problem we are focused on solving at Foxglove.

Adrian Macneil

101,245 views • 4 days ago

40:20

We need the data centers. We have to win the AI race. My guest for episode 5 of Counterweight is Manhattan Institute's Judge Glock. This spring he wrote about Loudoun County, VA in City Journal. Loudoun, one of the richest counties in America, with a median household income of around $170,000, has become the unlikely center of a new data center-based industrial revolution. And it's not just Loudoun. Data center construction is up about twenty times nationwide in three years, leading to an enormous political backlash. New York State imposed a moratorium on data center construction, and even Republican Greg Abbott of Texas has put a temporary pause in place. Meanwhile, speaking for myself, I’ve seen a ton of fear-mongering about them, with very little in the way of clear explanation of what the costs and benefits are and what they mean for the localities that might bring them in. I've brought Judge on to walk through in granular detail how data centers work, how Loudoun County became ground zero for the data center boom, and the recent pushback against data centers. We also talk about the unfortunate habit that AI CEOs have of seeming like the gloomiest people in the world in relation to the products they are trying to sell.

Will Chamberlain

21,276 views • 7 days ago

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

11,442 views • 3 months ago

43:11

NEW: Inside AI's Biggest Downstream Winner.. the Surge in AI Database Demand "Data is the unsung hero, & data is back." MongoDB CEO CJ Desai (CJ Desai) The unexpected result? Hyperscalers are turning away even top-50 accounts. "Sorry, we don't have a capacity." "And they are one of the top 50 customers for that hyperscaler." ElevenLabs alone runs "north of 50 million agents, depending on when you look at it, all running on MongoDB.. that gives us a lot of confidence that we have the right architecture for agentic workloads." NASDAQ: $MDB We cover: › The 3 classes of AI customers MongoDB serves › Why hyperscalers are telling top-50 accounts "no capacity" › On-prem, sovereign AI, and the data-center comeback › ElevenLabs running 50M+ agents on MongoDB › MongoDB (OLTP) vs Snowflake & Databricks (OLAP) › Why there is no standardization in enterprise AI models › Auto-scaling & the fall of expensive DBAs 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) CJ Desai, CEO at MongoDB (01:12) Top CEOs at the Raise AI Summit (03:24) The 3 types of companies powering the AI boom (07:25) Why on-prem is making a shocking comeback (09:01) Hyperscalers are quietly running out of capacity (11:06) What actually separates MongoDB from Snowflake x Databricks (13:18) Why Frontier Labs treat MongoDB as their memory layer (16:22) From Oracle intern to first-time CEO (19:19) Becoming CEO during the AI chaos (23:53) The World Cup crisis that tested MongoDB's scale (28:19) The real complexity behind simple AI agents (31:22) Open source vs. closed models: what customers actually pick (34:36) CJ's honest take on data centers in space (36:50) The mentors who shaped a first-time CEO (40:32) MongoDB's next big bets (41:36) Data is back?

Molly O’Shea

170,730 views • 1 month ago

1:31

Spirit Airlines stopped flying in May. Second bankruptcy in two years. Yet Google still wants to pay $10Million for the dead body. The planes are gone, the airport slots are sold. One asset left. At the bankruptcy auction, Google opened at 5 million dollars. An AI data company called Mercor countered at 7 and a half million. Then Google closed it at 10 Million. But the bids weren't for the planes or Airport slots. They were for 100 million internal emails. 500 million Teams messages. 30 million lines of code. Employee records that go back to 1986. And no, its not passenger data. This is purely internal: decades of how a real business thought, argued, and made decisions. Why pay that much for a dead company's inbox? Because it's the one thing AI can't fake. The most valuable data in the world right now is just real people thinking out loud… real decisions, real mistakes, real cause and effect. Which is why after the auction closed, another AI company came in with a 12 and a half million offer for it. Here's how you can leverage this kind of data for your business without a bankruptcy auction. People type their real, unfiltered questions into a search bar every single day… for free. And for business owners, those raw questions are a free roadmap: they tell you exactly what to create, what to fix on your site, and what to sell next. Tools like AnswerThePublic mine and present that exact data by looking at the different ways people search for a product. The AI data gold rush is just getting started, follow to not miss out!

Neil Patel

12,632 views • 5 days ago

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