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Alexandr Wang (Alexandr Wang) started Scale AI to help machine learning teams label data faster. It started as a simple API for human labor, but behind the scenes, he was tackling a much bigger problem: how to turn messy, real-world data into something AI could learn from. Today, that...

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396,950 görüntüleme • 1 yıl önce •via X (Twitter)

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@alexandr_wang I believe prioritize building tools that don’t just automate but amplify human judgment, because messy real-world data won’t clean itself. Layering solid APIs on human-in-the-loop workflows is where the real magic starts.

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@alexandr_wang Wow, that's amazing! I'm curious, what do you think sets Scale AI apart from other companies in the AI industry?

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@alexandr_wang This journey is founder fuel. Want me to turn it into a landing page in 3 min?

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@alexandr_wang Incredible journey. From labeling data to powering AI at national scale — true infrastructure play. @vendorapp, we’re on a similar path in vendor ops: turning messy workflows into structured, scalable systems that teams can actually build on. ⚙️📊 #AIinfra #StartupToScale

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@alexandr_wang In the future there will be only “ai stacks”. Tech stacks are dead

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@alexandr_wang I love seeing people win 😍 this man probably had hundreds of sleepless nights and kept pushing tru!! My journey just started and it has a small greenlight from grok himself publicly! I wonder what my journey will bring me.🙏

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@alexandr_wang Fantastic interview. I learned quite a bit. Also, thank you for the YC Startup AI event yesterday. It’s was so great to see the all the brilliant next gen thinkers and leaders gathered here together in SF.

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@alexandr_wang I don't think we should compare ourselves to alexander, what he did is insanely impressive but it's a mix of right time and an outlier scenario, like the dot com bubble I feel. 99% of founders aren't the bill gates, the zuck, but wang is in that 1% for sure.

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Leigh Marie Braswell (joined Scale AI at 3 employees) shares Alexandr Wang's vision & intuition: "Alex & his ability to put himself in the right rooms, know the right people, & be able to see.. where's all the revenue going to come from? From the next wave of what's going on in machine learning, human intelligence & data will always be needed. And I have full confidence, because he's done it time & time again, that Alex is always going to be able to figure out where is that place where Scale needs to build a product to sort of be able to bring the humans to the models." "Leigh Marie" Braswell Alexandr Wang Scale AI
0:46

Leigh Marie Braswell (joined Scale AI at 3 employees) shares Alexandr Wang's vision & intuition: "Alex & his ability to put himself in the right rooms, know the right people, & be able to see.. where's all the revenue going to come from? From the next wave of what's going on in machine learning, human intelligence & data will always be needed. And I have full confidence, because he's done it time & time again, that Alex is always going to be able to figure out where is that place where Scale needs to build a product to sort of be able to bring the humans to the models." "Leigh Marie" Braswell Alexandr Wang Scale AI

Molly O’Shea

66,174 görüntüleme • 1 yıl önce

ANTHROPIC JUST RELEASED A FREE 23-PAGE GUIDE THAT COMPANIES ARE PAYING $50,000 TO CONSULTANTS FOR. How to build Claude into your entire business infrastructure - 100% FREE. Most companies using AI in 2026 are doing the same thing. Anthropic explains what real enterprise AI infrastructure actually looks like. Not marketing, not a sales deck, just a practical guide from the people who build these systems. What is inside: ↳ How to move Claude from a chat tool to core business infrastructure ↳ How to connect it to your internal databases and live documents ↳ How to design automated workflows for your exact processes ↳ How to handle institutional data safely at scale The most important line in the whole document: The model alone is not enough. The value is in how it connects to your systems, data, and workflows. Save this.
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ANTHROPIC JUST RELEASED A FREE 23-PAGE GUIDE THAT COMPANIES ARE PAYING $50,000 TO CONSULTANTS FOR. How to build Claude into your entire business infrastructure - 100% FREE. Most companies using AI in 2026 are doing the same thing. Anthropic explains what real enterprise AI infrastructure actually looks like. Not marketing, not a sales deck, just a practical guide from the people who build these systems. What is inside: ↳ How to move Claude from a chat tool to core business infrastructure ↳ How to connect it to your internal databases and live documents ↳ How to design automated workflows for your exact processes ↳ How to handle institutional data safely at scale The most important line in the whole document: The model alone is not enough. The value is in how it connects to your systems, data, and workflows. Save this.

Kanika

16,545 görüntüleme • 1 ay önce

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 görüntüleme • 1 ay önce

Had a great talk with Anjney Midha about why he left a16z to start AMP PBC (raised $1.3B), his early bet on Anthropic, the importance of Japan in AI, his class at Stanford and a lot more. I didn't make him cry like Harry Stebbings, but I think it was a fun conversation about the future of AI Infra. Enjoy! 00:00 Introduction to AMP and AI Infrastructure 03:07 Transition from A16Z to AMP 06:01 Understanding AMP's Role in AI Compute 08:57 The Concept of a Compute Grid 11:59 The Need for Standardization in AI Infrastructure 14:59 Education and Collaboration in AI Development 17:59 Global Perspectives on AI Infrastructure 21:03 Japan's Role in AI and Robotics 23:14 Cultural Connections: Japan and Germany 25:08 The Journey to Teaching at Stanford 26:04 Security in Infrastructure: Lessons Learned 27:50 Cybersecurity and AI: A New Frontier 29:27 The Importance of Redundancy in Cybersecurity 30:39 Investing in AI: The Anthropic Experience 32:54 Wealth Creation and Public Participation 34:06 Infrastructure Bottlenecks in AI Development 36:57 Global Perspectives on Data Centers 39:11 The Need for Education on AI Benefits 42:07 Learning from China: A Systems Approach 45:47 Conclusion: Coordinating the Western Frontier
46:14

Had a great talk with Anjney Midha about why he left a16z to start AMP PBC (raised $1.3B), his early bet on Anthropic, the importance of Japan in AI, his class at Stanford and a lot more. I didn't make him cry like Harry Stebbings, but I think it was a fun conversation about the future of AI Infra. Enjoy! 00:00 Introduction to AMP and AI Infrastructure 03:07 Transition from A16Z to AMP 06:01 Understanding AMP's Role in AI Compute 08:57 The Concept of a Compute Grid 11:59 The Need for Standardization in AI Infrastructure 14:59 Education and Collaboration in AI Development 17:59 Global Perspectives on AI Infrastructure 21:03 Japan's Role in AI and Robotics 23:14 Cultural Connections: Japan and Germany 25:08 The Journey to Teaching at Stanford 26:04 Security in Infrastructure: Lessons Learned 27:50 Cybersecurity and AI: A New Frontier 29:27 The Importance of Redundancy in Cybersecurity 30:39 Investing in AI: The Anthropic Experience 32:54 Wealth Creation and Public Participation 34:06 Infrastructure Bottlenecks in AI Development 36:57 Global Perspectives on Data Centers 39:11 The Need for Education on AI Benefits 42:07 Learning from China: A Systems Approach 45:47 Conclusion: Coordinating the Western Frontier

Nathan Lands

28,786 görüntüleme • 2 ay önce

What does it take for CTOs and CIOs to build an AI stack where data reaches compute reliably, securely, and at scale? That’s the strategic question behind my conversation with Kunal Anand, CPO at #F5. As AI moves into production, the real differentiator won’t just be the model — it will be the infrastructure that delivers data with reliability, security, and visibility. The last mile of AI is becoming a first-class product decision, not just an infrastructure one. Watch the full conversation: Register for AI Virtual Summit (free) #AI #CTO #CIO #EnterpriseAI #Infrastructure #Cybersecurity #F5Partner
1:02

What does it take for CTOs and CIOs to build an AI stack where data reaches compute reliably, securely, and at scale? That’s the strategic question behind my conversation with Kunal Anand, CPO at #F5. As AI moves into production, the real differentiator won’t just be the model — it will be the infrastructure that delivers data with reliability, security, and visibility. The last mile of AI is becoming a first-class product decision, not just an infrastructure one. Watch the full conversation: Register for AI Virtual Summit (free) #AI #CTO #CIO #EnterpriseAI #Infrastructure #Cybersecurity #F5Partner

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42,853 görüntüleme • 1 yıl önce

அன்றே கணித்தார் Sriram Krishnan A year ago, on the Good time show, Sriram Krishnan and Aarthi Ramamurthy, along with Alexandr Wang, discussed how crypto can solve the problem of AI data attribution. They also talked about how, when a synthetic AI generation is created from a base data IP and a derivative IP is generated, the base data's IP owner should still receive rightful royalties. At the time, many weren’t aware of the significance, but now this has become a key focus for every DEAI crypto company. At Openledger, our mission is to build an AI DATA infrastructure from start to finish. We gather data from people, create personas from that data, attribute the right contributors—whether for data contribution or synthetic personas (derivatives)—and provide incentives. If you’re a tech founder, make sure to check out Aarthi and Sriram’s podcast at to get some real tech insights.
5:10

அன்றே கணித்தார் Sriram Krishnan A year ago, on the Good time show, Sriram Krishnan and Aarthi Ramamurthy, along with Alexandr Wang, discussed how crypto can solve the problem of AI data attribution. They also talked about how, when a synthetic AI generation is created from a base data IP and a derivative IP is generated, the base data's IP owner should still receive rightful royalties. At the time, many weren’t aware of the significance, but now this has become a key focus for every DEAI crypto company. At Openledger, our mission is to build an AI DATA infrastructure from start to finish. We gather data from people, create personas from that data, attribute the right contributors—whether for data contribution or synthetic personas (derivatives)—and provide incentives. If you’re a tech founder, make sure to check out Aarthi and Sriram’s podcast at to get some real tech insights.

Kamesh 𝐎𝐩𝐞𝐧𝐋𝐞𝐝𝐠𝐞𝐫 🇺🇸

24,736 görüntüleme • 1 yıl önce

Alexandr Wang on why Paul Graham’s “Schlep Blindness” essay was seminal for Scale AI “One of the secrets to Scale AI — and I think this applies to almost every industry — was that the problem we were solving of building really high quality data sets was something that most machine learning teams knew was very important but it wasn’t necessarily the sexiest problem that every AI scientist wanted to spend their days and nights working on.” Alexandr continues: “There was one article that was pretty seminal for me early on. It was an essay by Paul Graham called ‘Schlep Blindness.’ I’d encourage everyone to read it if you get a chance. But basically the idea was that most people avoid thinking about the really difficult, hairy, ugly, and annoying problems that exist in the world but they’re really important. He actually uses Stripe as one of the examples in his essay, but these problems are everywhere. The ugly, hairy problems that everyone knows are important but aren’t sexy to work on — if you can identify what those problems are, they generally make really exciting startup ideas.” This was a lot of the original pitch for Scale: “You know this is important but you probably aren’t the most excited to work on it.” And then the early Scale team was super scrappy, which helped them earn the trust of their customers: “They saw our product velocity and how fast we were moving. They thought to themselves, ‘Even if they don’t have the perfect product today, they’re going to get to a product that we’re going to be able to rely on really quickly.’” Source: Startup Grind (Apr 2022)
2:03

Alexandr Wang on why Paul Graham’s “Schlep Blindness” essay was seminal for Scale AI “One of the secrets to Scale AI — and I think this applies to almost every industry — was that the problem we were solving of building really high quality data sets was something that most machine learning teams knew was very important but it wasn’t necessarily the sexiest problem that every AI scientist wanted to spend their days and nights working on.” Alexandr continues: “There was one article that was pretty seminal for me early on. It was an essay by Paul Graham called ‘Schlep Blindness.’ I’d encourage everyone to read it if you get a chance. But basically the idea was that most people avoid thinking about the really difficult, hairy, ugly, and annoying problems that exist in the world but they’re really important. He actually uses Stripe as one of the examples in his essay, but these problems are everywhere. The ugly, hairy problems that everyone knows are important but aren’t sexy to work on — if you can identify what those problems are, they generally make really exciting startup ideas.” This was a lot of the original pitch for Scale: “You know this is important but you probably aren’t the most excited to work on it.” And then the early Scale team was super scrappy, which helped them earn the trust of their customers: “They saw our product velocity and how fast we were moving. They thought to themselves, ‘Even if they don’t have the perfect product today, they’re going to get to a product that we’re going to be able to rely on really quickly.’” Source: Startup Grind (Apr 2022)

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We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

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AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.

Mario Nawfal

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.Poetiq is a new startup that recently achieved a major jump on the ARC-AGI benchmark by layering a recursive self-improvement system on top of existing models. In this episode of the Lightcone Podcast, Poetiq's Founder & CEO Ian Fischer joined us to discuss how small teams can build “reasoning harnesses” that outperform base models, what that means for startups and why automating prompt engineering may be one of the most powerful levers in AI today. 00:00 – Intro 00:40 – What Is Poetiq? 01:07 – Recursive Self-Improvement Explained 02:07 – The Fine-Tuning Trap 02:59 – “Stilts” for LLMs 03:14 – Recursive Self-Improvement vs. Fine-Tuning 05:05 – Taking the Top Spot on ARC-AGI 06:37 – Beating Claude on Humanity’s Last Exam 08:40 – How the Meta-System Works 10:26 – Beyond RL: A New S-Curve 11:32 – Automating Prompt Engineering 13:37 – From 5% to 95% Performance 14:50 – Early Access & Putting Your Agent on Stilts 16:17 – From YC Founder to DeepMind Researcher 18:29 – Advice for Engineers in the AI Era
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.Poetiq is a new startup that recently achieved a major jump on the ARC-AGI benchmark by layering a recursive self-improvement system on top of existing models. In this episode of the Lightcone Podcast, Poetiq's Founder & CEO Ian Fischer joined us to discuss how small teams can build “reasoning harnesses” that outperform base models, what that means for startups and why automating prompt engineering may be one of the most powerful levers in AI today. 00:00 – Intro 00:40 – What Is Poetiq? 01:07 – Recursive Self-Improvement Explained 02:07 – The Fine-Tuning Trap 02:59 – “Stilts” for LLMs 03:14 – Recursive Self-Improvement vs. Fine-Tuning 05:05 – Taking the Top Spot on ARC-AGI 06:37 – Beating Claude on Humanity’s Last Exam 08:40 – How the Meta-System Works 10:26 – Beyond RL: A New S-Curve 11:32 – Automating Prompt Engineering 13:37 – From 5% to 95% Performance 14:50 – Early Access & Putting Your Agent on Stilts 16:17 – From YC Founder to DeepMind Researcher 18:29 – Advice for Engineers in the AI Era

Y Combinator

155,843 görüntüleme • 4 ay önce

DeepSeek: How US Sanctions Help Chinese Innovation | Tyler Cowen on the Paradox of Control tylercowen was the first columnist to break the news on DeepSeek, an advanced Chinese AI, weeks before the markets reacted. His claim is that by restricting China’s high-end chip supply, the U.S. unintentionally encouraged China to pioneer techniques in cost-effective AI training. We kick off this interview by discussing what this “Sputnik moment” has to teach us about U.S.-China relations, counter-espionage, and the philosophy of innovation. Later in the conversation, Tyler shares his predictions on how AI will transform philosophy, knowledge work, warfare, economics, and everything in between. For example, Tyler thinks that OpenAI’s o1 pro is ALREADY better at reflecting on complex questions at the frontier of economics. We discuss what that means for the future of work and philosophy, and where you should invest your skills and time today. For me, the most valuable takeaways from this conversation are all the practical tips Tyler has to share about how we can stay ahead of the AI curve and not be made obsolete. Timestamps: 02:37 AI Is Neither Libertarian nor Communist, but chaotic 03:53 Can the U.S. Restrict China's AI Advances? 04:47 How the U.S. Unintentionally Jump-started China’s Space Program 05:29 The Only Way to Protect American Technology is Openness 10:35 Girard’s Philosophy of Innovation 13:26 The American Case for Stealing Technology 15:55 How to Live in “Moving History” 24:34 Why Most Reactions to AI are Cope 33:20 Can Democracy Survive AI? 37:19 Why Tyler Writes Primarily for AIs (not Humans) 49:58 Most Intellectual Work will be Automated 01:00:03 Why Travel Will Matter More in the AI Era
1:05:25

DeepSeek: How US Sanctions Help Chinese Innovation | Tyler Cowen on the Paradox of Control tylercowen was the first columnist to break the news on DeepSeek, an advanced Chinese AI, weeks before the markets reacted. His claim is that by restricting China’s high-end chip supply, the U.S. unintentionally encouraged China to pioneer techniques in cost-effective AI training. We kick off this interview by discussing what this “Sputnik moment” has to teach us about U.S.-China relations, counter-espionage, and the philosophy of innovation. Later in the conversation, Tyler shares his predictions on how AI will transform philosophy, knowledge work, warfare, economics, and everything in between. For example, Tyler thinks that OpenAI’s o1 pro is ALREADY better at reflecting on complex questions at the frontier of economics. We discuss what that means for the future of work and philosophy, and where you should invest your skills and time today. For me, the most valuable takeaways from this conversation are all the practical tips Tyler has to share about how we can stay ahead of the AI curve and not be made obsolete. Timestamps: 02:37 AI Is Neither Libertarian nor Communist, but chaotic 03:53 Can the U.S. Restrict China's AI Advances? 04:47 How the U.S. Unintentionally Jump-started China’s Space Program 05:29 The Only Way to Protect American Technology is Openness 10:35 Girard’s Philosophy of Innovation 13:26 The American Case for Stealing Technology 15:55 How to Live in “Moving History” 24:34 Why Most Reactions to AI are Cope 33:20 Can Democracy Survive AI? 37:19 Why Tyler Writes Primarily for AIs (not Humans) 49:58 Most Intellectual Work will be Automated 01:00:03 Why Travel Will Matter More in the AI Era

Johnathan Bi

49,815 görüntüleme • 1 yıl önce

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.
1:06

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

Six Five Media

7,422,478 görüntüleme • 3 ay önce

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly
0:30

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly

Adaptyv Bio

13,446 görüntüleme • 1 ay önce

Bob McGrew (Bob McGrew) helped build some of the most influential technologies of the past two decades. He was an early engineer at PayPal, an early executive at Palantir—where he helped pioneer the Forward Deployed Engineer (FDE) model— and was recently Chief Research Officer at OpenAI - where he led the development of ChatGPT, GPT-4, and o1. On this episode of Lightcone Podcast, he explains how FDEs became central to today's startups, why "doing things that don't scale at scale" works, and where he sees the biggest opportunities for founders working in AI. 00:29 – From PayPal to Palantir to OpenAI 02:19 – The Role of a Forward Deployed Engineer 03:19 – How Palantir Invented It 07:56 – Product Discovery in the Field vs. Sales 09:51 – Echo and Delta Teams Explained 13:34 – Training Ground for Founders 14:35 – Consulting or Real Software? 17:54 – The Birth of Palantir’s Ontology 23:04 – Why AI Companies Adopt It 36:17 – What Success Metrics Look Like 41:14 – Building with Demo-Driven Development 44:56 – Joining the US Army Reserve 47:43 – Opportunities for Founders
50:42

Bob McGrew (Bob McGrew) helped build some of the most influential technologies of the past two decades. He was an early engineer at PayPal, an early executive at Palantir—where he helped pioneer the Forward Deployed Engineer (FDE) model— and was recently Chief Research Officer at OpenAI - where he led the development of ChatGPT, GPT-4, and o1. On this episode of Lightcone Podcast, he explains how FDEs became central to today's startups, why "doing things that don't scale at scale" works, and where he sees the biggest opportunities for founders working in AI. 00:29 – From PayPal to Palantir to OpenAI 02:19 – The Role of a Forward Deployed Engineer 03:19 – How Palantir Invented It 07:56 – Product Discovery in the Field vs. Sales 09:51 – Echo and Delta Teams Explained 13:34 – Training Ground for Founders 14:35 – Consulting or Real Software? 17:54 – The Birth of Palantir’s Ontology 23:04 – Why AI Companies Adopt It 36:17 – What Success Metrics Look Like 41:14 – Building with Demo-Driven Development 44:56 – Joining the US Army Reserve 47:43 – Opportunities for Founders

Y Combinator

408,714 görüntüleme • 10 ay önce

The German Federal Innovation Agency is running a €125m competition to fund 3 new Frontier AI labs in Europe! "Next Frontier AI" is a pan-European initiative led by SPRIND, Federal Agency for Breakthrough Innovation to create three globally relevant Frontier AI labs. It starts with a €125m challenge where up to 10 teams receive funding over 24 months to build frontier-lab capabilities, including team, infrastructure, prototypes, evaluation discipline, and early pilots. The best-performing teams will then receive structured support toward raising ~€1 billion each. Europe has some of the top AI companies in the world, but as a continent we are massively dependent on frontier labs from other countries. This initiative seeks to change that and I am HERE FOR IT.
1:30

The German Federal Innovation Agency is running a €125m competition to fund 3 new Frontier AI labs in Europe! "Next Frontier AI" is a pan-European initiative led by SPRIND, Federal Agency for Breakthrough Innovation to create three globally relevant Frontier AI labs. It starts with a €125m challenge where up to 10 teams receive funding over 24 months to build frontier-lab capabilities, including team, infrastructure, prototypes, evaluation discipline, and early pilots. The best-performing teams will then receive structured support toward raising ~€1 billion each. Europe has some of the top AI companies in the world, but as a continent we are massively dependent on frontier labs from other countries. This initiative seeks to change that and I am HERE FOR IT.

Seb Johnson

24,124 görüntüleme • 3 ay önce

“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Miyasaki sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.
5:35

“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Miyasaki sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.

BeaconLayer Podcast

73,815 görüntüleme • 1 ay önce

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 görüntüleme • 1 yıl önce

Agentic commerce is coming 🔥 — Emily GlassbergSands, Head of Information at Stripe unpacks how autonomous “buying bots” and MCP are reshaping the very mechanics of online transactions - and how Stripe’s AI models and rails are adapting for tokens, one-time virtual cards, and real-time risk management. 0:00 - Intro, latest Stripe stats 01:45 - Backgroud: The “Head of Information” role at Stripe, and Emily's journey from Harvard to Stripe 08:54 - Why Stripe Built Its Own Foundation Model 13:19 - Cracking the Code: How Stripe Handles Complex Payment Data 16:25 - Foundation Model vs. Traditional ML: What’s Winning, When? 20:09 - Inside Stripe’s Foundation Model: How It Was Built 24:35 - Transparency vs black box AI 28:38 - How to decide when to use AI and when not 34:10 - How Stripe’s AI Drives Revenue for Businesses 41:22 - Real-Time Fraud Detection: Stripe’s Secret Sauce 42:51 - The Future of Shopping: AI Agents & Agentic Commerce 46:20 - How Agentic Commerce Is Changing Stripe 49:36 - Stripe’s Vision for a World of AI-Powered Buyers 55:46 - What Is MCP? Stripe’s Take on Agent-to-Agent Protocols 59:31 - Stripe’s Data on AI Startups Monetizing 3× Faster 1:03:03 - How AI Companies Go Global — From Day One 1:07:48 - The New Rules: Billing & Pricing for AI Startups 1:10:57 - How Stripe Builds AI Literacy Across the Company 1:14:05 - Roadmap: Risk-as-a-Service, Order Intent, and Beyond
1:15:14

Agentic commerce is coming 🔥 — Emily GlassbergSands, Head of Information at Stripe unpacks how autonomous “buying bots” and MCP are reshaping the very mechanics of online transactions - and how Stripe’s AI models and rails are adapting for tokens, one-time virtual cards, and real-time risk management. 0:00 - Intro, latest Stripe stats 01:45 - Backgroud: The “Head of Information” role at Stripe, and Emily's journey from Harvard to Stripe 08:54 - Why Stripe Built Its Own Foundation Model 13:19 - Cracking the Code: How Stripe Handles Complex Payment Data 16:25 - Foundation Model vs. Traditional ML: What’s Winning, When? 20:09 - Inside Stripe’s Foundation Model: How It Was Built 24:35 - Transparency vs black box AI 28:38 - How to decide when to use AI and when not 34:10 - How Stripe’s AI Drives Revenue for Businesses 41:22 - Real-Time Fraud Detection: Stripe’s Secret Sauce 42:51 - The Future of Shopping: AI Agents & Agentic Commerce 46:20 - How Agentic Commerce Is Changing Stripe 49:36 - Stripe’s Vision for a World of AI-Powered Buyers 55:46 - What Is MCP? Stripe’s Take on Agent-to-Agent Protocols 59:31 - Stripe’s Data on AI Startups Monetizing 3× Faster 1:03:03 - How AI Companies Go Global — From Day One 1:07:48 - The New Rules: Billing & Pricing for AI Startups 1:10:57 - How Stripe Builds AI Literacy Across the Company 1:14:05 - Roadmap: Risk-as-a-Service, Order Intent, and Beyond

Matt Turck

17,720 görüntüleme • 1 yıl önce