Загрузка видео...

Не удалось загрузить видео

На главную

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...

MollySOShea's profile picture

Molly O’Shea

71,363 subscribers

170,522 просмотров • 1 месяц назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

MongoDB CEO CJ Desai (CJ Desai) says ElevenLabs has “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.”
2:07

MongoDB CEO CJ Desai (CJ Desai) says ElevenLabs has “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.”

Molly O’Shea

62,267 просмотров • 1 месяц назад

BREAKING: Inside Snowflake (NYSE: SNOW) with CEO Sridhar Ramaswamy Fresh off the slopes of Davos to Snowflake’s HQ, Sridhar shares $SNOW plans to win the AI data platform race. We cover: - The AI platform war (Snowflake vs. Databricks, hyperscalers) - Where AI value accrues (Nvidia, frontier models, enterprise workflows) - Eric Schmidt’s $100B revenue playbook at Google - Frank Slootman’s CEO transition & leadership style - Sutter Hill’s influence - The recent Observe acquisition & AI observability with rise of AI agents - Trillion-dollar IPOs & valuation psychology - Coding agents compressing costs by ~10x - Ramaswamy’s “monk mode” discipline Highlights: (00:00) Sridhar Ramaswamy, CEO Snowflake (01:03) Data centers in space & why the economics still feels unclear (03:06) AI demand explosion & why old chips & models still matter (04:36) Where are we exactly in the AI supercycle? (06:41) Where value is being created vs where value is accruing (10:30) Hyperscalers, dominance, & shrinking AI moats (12:21) AI adoption in financial services, healthcare, & real use cases (16:01) Snowflake’s role in the AI supercycle (18:26) Shifting from long-term plans to week-by-week execution (20:39) Why this AI moment feels terrifying for technologists (23:16) Sutter Hill, board dynamics (24:26) Snowflake’s trillion-dollar ambition & lessons from Eric Schmidt (26:47) Leading teams through constant change (28:43) What does Snowflake look for when hiring? (30:51) Lessons from Frank Slootman & wartime leadership (36:12) Which companies will fail in the AI era (38:30) Monk mode (39:36) Acquiring Observe & integrating acquisitions without killing momentum (43:11) $1T IPOs, valuation discipline, & investor psychology (45:11) Managing investors & long-term stewardship
47:36

BREAKING: Inside Snowflake (NYSE: SNOW) with CEO Sridhar Ramaswamy Fresh off the slopes of Davos to Snowflake’s HQ, Sridhar shares $SNOW plans to win the AI data platform race. We cover: - The AI platform war (Snowflake vs. Databricks, hyperscalers) - Where AI value accrues (Nvidia, frontier models, enterprise workflows) - Eric Schmidt’s $100B revenue playbook at Google - Frank Slootman’s CEO transition & leadership style - Sutter Hill’s influence - The recent Observe acquisition & AI observability with rise of AI agents - Trillion-dollar IPOs & valuation psychology - Coding agents compressing costs by ~10x - Ramaswamy’s “monk mode” discipline Highlights: (00:00) Sridhar Ramaswamy, CEO Snowflake (01:03) Data centers in space & why the economics still feels unclear (03:06) AI demand explosion & why old chips & models still matter (04:36) Where are we exactly in the AI supercycle? (06:41) Where value is being created vs where value is accruing (10:30) Hyperscalers, dominance, & shrinking AI moats (12:21) AI adoption in financial services, healthcare, & real use cases (16:01) Snowflake’s role in the AI supercycle (18:26) Shifting from long-term plans to week-by-week execution (20:39) Why this AI moment feels terrifying for technologists (23:16) Sutter Hill, board dynamics (24:26) Snowflake’s trillion-dollar ambition & lessons from Eric Schmidt (26:47) Leading teams through constant change (28:43) What does Snowflake look for when hiring? (30:51) Lessons from Frank Slootman & wartime leadership (36:12) Which companies will fail in the AI era (38:30) Monk mode (39:36) Acquiring Observe & integrating acquisitions without killing momentum (43:11) $1T IPOs, valuation discipline, & investor psychology (45:11) Managing investors & long-term stewardship

Molly O’Shea

62,738 просмотров • 7 месяцев назад

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich
2:03

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

Helen Yu

10,982 просмотров • 1 месяц назад

NEW: Cerebras $CBRS CEO Andrew Feldman (Andrew Feldman) "When the chip on your shoulder is the largest chip the world has ever seen." "The demand for AI has outpaced everybody's expectation & everybody's forecast. & so everybody's chasing. They're chasing chips, memory, or data centers." We get into the chip 58x larger than any other, $20B OpenAI deal signed in 4.5 weeks, & what's actually going on with the 'big AI deals' Recorded 2 months after Cerebras' $5.5B IPO at a $56B valuation, where a first-day pop briefly hit ~$95B before settling toward ~$60B. We cover: › A "Cambrian explosion" of new chip architectures › $20B+ OpenAI deal: 750MW of inference compute over 3 years › Why inference, not training, is where the value is now › "We are behind" on the data center build-out › Free tokens & circular deals: "These are drug pushers" › Creating 1,000 millionaires › Nvidia's balance sheet & market strength › Sovereign AI & owning the stack › Co-design & data centers in space › A real 25-year path to ending cancer Cerebras builds AI infrastructure for training & inference. It went public in May 2026, & its products include inference, Wafer Scale Engine, AI supercomputers, AI model services, cloud, systems, & processors. Filmed at the Raise Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Andrew Feldman, Co-Founder & CEO at Cerebras Systems (00:49) Why hardware suddenly became the coolest industry in tech (01:51) What changed at Raise AI Summit (03:01) Inside the $20 billion Cerebras - OpenAI deal (05:50) What actually changes two months after an IPO (06:32) Turning 1,000 employees into millionaires (07:52) Staying sane during an AI gold rush (10:44) Life after the IPO plateau (11:45) The truth about the global data center shortage (12:56) Why data centers are borrowing jet engines for power (14:44) Are data centers really headed to space? (15:37) The shift to designing chips & software together (17:39) The biggest misconception about co-designing chips & software (18:31) Inside SpaceX's multi-billion dollar AI deals (20:06) NVIDIA's playbook for locking out competitors (20:54) The hidden cost behind free tokens (22:52) Andrew's response to Karp's sovereign AI thesis (24:18) AI's biggest win might be curing cancer (26:50) Peptides & biohacking (28:00) How AI could finally fix the broken education problem (29:46) The mentors who shaped Andrew Feldman's career
33:00

NEW: Cerebras $CBRS CEO Andrew Feldman (Andrew Feldman) "When the chip on your shoulder is the largest chip the world has ever seen." "The demand for AI has outpaced everybody's expectation & everybody's forecast. & so everybody's chasing. They're chasing chips, memory, or data centers." We get into the chip 58x larger than any other, $20B OpenAI deal signed in 4.5 weeks, & what's actually going on with the 'big AI deals' Recorded 2 months after Cerebras' $5.5B IPO at a $56B valuation, where a first-day pop briefly hit ~$95B before settling toward ~$60B. We cover: › A "Cambrian explosion" of new chip architectures › $20B+ OpenAI deal: 750MW of inference compute over 3 years › Why inference, not training, is where the value is now › "We are behind" on the data center build-out › Free tokens & circular deals: "These are drug pushers" › Creating 1,000 millionaires › Nvidia's balance sheet & market strength › Sovereign AI & owning the stack › Co-design & data centers in space › A real 25-year path to ending cancer Cerebras builds AI infrastructure for training & inference. It went public in May 2026, & its products include inference, Wafer Scale Engine, AI supercomputers, AI model services, cloud, systems, & processors. Filmed at the Raise Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Andrew Feldman, Co-Founder & CEO at Cerebras Systems (00:49) Why hardware suddenly became the coolest industry in tech (01:51) What changed at Raise AI Summit (03:01) Inside the $20 billion Cerebras - OpenAI deal (05:50) What actually changes two months after an IPO (06:32) Turning 1,000 employees into millionaires (07:52) Staying sane during an AI gold rush (10:44) Life after the IPO plateau (11:45) The truth about the global data center shortage (12:56) Why data centers are borrowing jet engines for power (14:44) Are data centers really headed to space? (15:37) The shift to designing chips & software together (17:39) The biggest misconception about co-designing chips & software (18:31) Inside SpaceX's multi-billion dollar AI deals (20:06) NVIDIA's playbook for locking out competitors (20:54) The hidden cost behind free tokens (22:52) Andrew's response to Karp's sovereign AI thesis (24:18) AI's biggest win might be curing cancer (26:50) Peptides & biohacking (28:00) How AI could finally fix the broken education problem (29:46) The mentors who shaped Andrew Feldman's career

Molly O’Shea

494,613 просмотров • 1 месяц назад

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

70,007 просмотров • 10 месяцев назад

EPISODE 158: Nima Ghamsari on the Power of AI Agents & Infinite Workforce Joe Lonsdale sits down with Nima Ghamsari to cover some favorite stories from Palantir, why he's all-in on AI agents, Blend's comeback story, and more. (00:00) Episode intro (01:45) Making millions playing poker to joining Palantir (06:00) Most important lessons from Palantir (07:30) Founding $BLND / understanding the mortgage industry (11:25) How to win over banks and risk-averse customers (15:50) Blend's meteoric rise and fall (19:00) Going all-in on AI agents / Blend's Act II (22:55) efficient markets / is Blend undervalued today? (26:50) Impact of AI on internal operations / increased productivity (32:00) The future of SaaS (36:20) The optimistic case for AI
38:57

EPISODE 158: Nima Ghamsari on the Power of AI Agents & Infinite Workforce Joe Lonsdale sits down with Nima Ghamsari to cover some favorite stories from Palantir, why he's all-in on AI agents, Blend's comeback story, and more. (00:00) Episode intro (01:45) Making millions playing poker to joining Palantir (06:00) Most important lessons from Palantir (07:30) Founding $BLND / understanding the mortgage industry (11:25) How to win over banks and risk-averse customers (15:50) Blend's meteoric rise and fall (19:00) Going all-in on AI agents / Blend's Act II (22:55) efficient markets / is Blend undervalued today? (26:50) Impact of AI on internal operations / increased productivity (32:00) The future of SaaS (36:20) The optimistic case for AI

American Optimist

158,396 просмотров • 2 месяцев назад

"You should really treat having your own agent as having your own child. It's not one-click automation, you're raising it." Demi Guo (Demi Guo ), CEO of Pika on why agents need taste, not just prompts. Lin Qiao (Lin Qiao), CEO of Fireworks AI on why 95% of the world's data is still locked inside enterprises and why open-source models are converging with frontier. They join @jason on This Week in AI Episode 9: 00:00 Intro to Lin Qiao and Demi Guo 08:43 95% of private data locked in enterprises 16:03 The interface for creation is a human-like agent 22:07 The death of middle management 28:03 Taste, judgment, and why "slop" is the real risk 33:40 Agents aren't tools, they're children you raise 39:36 How close is open-source to frontier? 53:04 Agents as self-expression 63:17 Meta's Muse model goes closed-source
1:14:18

"You should really treat having your own agent as having your own child. It's not one-click automation, you're raising it." Demi Guo (Demi Guo ), CEO of Pika on why agents need taste, not just prompts. Lin Qiao (Lin Qiao), CEO of Fireworks AI on why 95% of the world's data is still locked inside enterprises and why open-source models are converging with frontier. They join @jason on This Week in AI Episode 9: 00:00 Intro to Lin Qiao and Demi Guo 08:43 95% of private data locked in enterprises 16:03 The interface for creation is a human-like agent 22:07 The death of middle management 28:03 Taste, judgment, and why "slop" is the real risk 33:40 Agents aren't tools, they're children you raise 39:36 How close is open-source to frontier? 53:04 Agents as self-expression 63:17 Meta's Muse model goes closed-source

This Week in AI

48,202 просмотров • 4 месяцев назад

"I don't want to rent myself back from Sam Altman. That is my personal Black Mirror." Kanjun 🐙 (Imbue) on why open source agents are the only way out. Carina Hong (Axiom) on why nobody should trust a single line of unverified AI code. Jonathan Siddharth (Turing) on the data loop making the models smarter. They join @jason on This Week in AI Episode 8: 00:00 Intro 01:31 Open source agents vs. total lock-in 05:10 AI that aced the hardest math exam 09:52 The superintelligence accelerator 15:53 Anthropic's $30B run rate 22:27 OpenAI's identity crisis 28:27 Meta's token-burning leaderboard 44:08 Commodification of developers 58:27 Superintelligence in 36 months 01:08:41 Big Tech is failing at AI
1:26:39

"I don't want to rent myself back from Sam Altman. That is my personal Black Mirror." Kanjun 🐙 (Imbue) on why open source agents are the only way out. Carina Hong (Axiom) on why nobody should trust a single line of unverified AI code. Jonathan Siddharth (Turing) on the data loop making the models smarter. They join @jason on This Week in AI Episode 8: 00:00 Intro 01:31 Open source agents vs. total lock-in 05:10 AI that aced the hardest math exam 09:52 The superintelligence accelerator 15:53 Anthropic's $30B run rate 22:27 OpenAI's identity crisis 28:27 Meta's token-burning leaderboard 44:08 Commodification of developers 58:27 Superintelligence in 36 months 01:08:41 Big Tech is failing at AI

This Week in AI

39,984 просмотров • 4 месяцев назад

NEW: Lumentum (NASDAQ: $LITE ) CEO Michael Hurlston on the Optics Boom How Lasers Are Transforming AI Data Centers "We're being asked by hyperscalers to deploy in millions, tens of millions of units." "You're not gonna have birds burning up as these lasers shoot data down to the Earth, right?" "Copper can't carry these signals the distances they need to go. You have more & more optics now coming into the data center." We cover: › Why AI is replacing copper with light › Birds vs. space lasers › Space lasers & the future of the internet › Why we're spending $10T moving data from kilometers to millimeters › Lumentum's 3x revenue growth in just 5 quarters › Stock went up 1000% › U.S.-China geopolitics in optical networking › The manufacturing bottlenecks facing optical infrastructure Michael Hurlston became President & CEO of Lumentum (LITE) on February 7, 2025. Fifteen months later the company reported record quarterly revenue of $808.4M, up 90% year over year, joined the Nasdaq-100, & took a $2B investment from Nvidia. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Michael Hurlston, CEO at Lumentum Holdings (00:58) Backstage with Tony Kim at RAISE Summit (01:53) What optical connectivity actually means (03:31) The $10 trillion irony of the data center build-out (05:37) Scaling up inside a data center (08:38) 3x revenue in 5 quarters (10:13) From Finisar to Synaptics to Lumentum (11:51) Why Lumentum went all-in on data centers (13:10) Why space is the next big market for optical lasers (14:06) Debunking laser myths (space vs birds) (16:40) Why copper is about to disappear from the server rack (19:00) The geopolitics of the optical supply chain (20:16) The advice that shaped Michael's path
23:56

NEW: Lumentum (NASDAQ: $LITE ) CEO Michael Hurlston on the Optics Boom How Lasers Are Transforming AI Data Centers "We're being asked by hyperscalers to deploy in millions, tens of millions of units." "You're not gonna have birds burning up as these lasers shoot data down to the Earth, right?" "Copper can't carry these signals the distances they need to go. You have more & more optics now coming into the data center." We cover: › Why AI is replacing copper with light › Birds vs. space lasers › Space lasers & the future of the internet › Why we're spending $10T moving data from kilometers to millimeters › Lumentum's 3x revenue growth in just 5 quarters › Stock went up 1000% › U.S.-China geopolitics in optical networking › The manufacturing bottlenecks facing optical infrastructure Michael Hurlston became President & CEO of Lumentum (LITE) on February 7, 2025. Fifteen months later the company reported record quarterly revenue of $808.4M, up 90% year over year, joined the Nasdaq-100, & took a $2B investment from Nvidia. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Michael Hurlston, CEO at Lumentum Holdings (00:58) Backstage with Tony Kim at RAISE Summit (01:53) What optical connectivity actually means (03:31) The $10 trillion irony of the data center build-out (05:37) Scaling up inside a data center (08:38) 3x revenue in 5 quarters (10:13) From Finisar to Synaptics to Lumentum (11:51) Why Lumentum went all-in on data centers (13:10) Why space is the next big market for optical lasers (14:06) Debunking laser myths (space vs birds) (16:40) Why copper is about to disappear from the server rack (19:00) The geopolitics of the optical supply chain (20:16) The advice that shaped Michael's path

Molly O’Shea

155,732 просмотров • 27 дней назад

2025 was the year AI stopped feeling chaotic and started feeling buildable. On the Lightcone Podcast, Garry Tan, Harj Taggar, Diana, and Jared Friedman break down the surprises of the year, from shifting model dominance to why the real opportunity is moving back to the application layer, and why the next wave of AI startups may be just getting started. 00:50 - Anthropic models are most preferred in the latest YC batch 05:36 - Why aren’t there more AI consumer apps? 07:01 - Swapping models in and out is becoming the norm 09:08 - The big AI bubble question 14:37 - Space as the solve for data centers and energy 17:29 - Interest in starting model companies 21:01 - Vibe coding became a big category 22:23 - AI economy has stabilized 23:38 - The AI 2027 piece 25:49 - Founders still need to hire teams
30:22

2025 was the year AI stopped feeling chaotic and started feeling buildable. On the Lightcone Podcast, Garry Tan, Harj Taggar, Diana, and Jared Friedman break down the surprises of the year, from shifting model dominance to why the real opportunity is moving back to the application layer, and why the next wave of AI startups may be just getting started. 00:50 - Anthropic models are most preferred in the latest YC batch 05:36 - Why aren’t there more AI consumer apps? 07:01 - Swapping models in and out is becoming the norm 09:08 - The big AI bubble question 14:37 - Space as the solve for data centers and energy 17:29 - Interest in starting model companies 21:01 - Vibe coding became a big category 22:23 - AI economy has stabilized 23:38 - The AI 2027 piece 25:49 - Founders still need to hire teams

Y Combinator

102,635 просмотров • 8 месяцев назад

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

36,985 просмотров • 8 месяцев назад

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 просмотров • 9 месяцев назад

If I could only follow one person on X, it would be Aaron Levie, CEO of Box. We sat down to speak about why the REAL economy hasn't felt AI yet, the "model overhang" between capability and adoption, and what founders should actually build now that software is cheap. 00:00 - Cold open: agents use data like people do 01:53 - Is Redwood City the new AI epicenter? 05:18 - 90% of enterprise data is unstructured and was never automatable 07:46 - The model overhang: capability is outpacing adoption 10:35 - Why coding got the fastest AI takeoff 16:24 - Inside Box's agent-first rebuild 19:15 - Headless SaaS: agents will outnumber humans 100:1 25:41 - Model routing + why the applied layer wins, even bitter-lesson-pilled 27:45 - GTM is the new moat Checkout Navigators, our new pod on Youtube, Spotify, and Apple Podcasts!
30:40

If I could only follow one person on X, it would be Aaron Levie, CEO of Box. We sat down to speak about why the REAL economy hasn't felt AI yet, the "model overhang" between capability and adoption, and what founders should actually build now that software is cheap. 00:00 - Cold open: agents use data like people do 01:53 - Is Redwood City the new AI epicenter? 05:18 - 90% of enterprise data is unstructured and was never automatable 07:46 - The model overhang: capability is outpacing adoption 10:35 - Why coding got the fastest AI takeoff 16:24 - Inside Box's agent-first rebuild 19:15 - Headless SaaS: agents will outnumber humans 100:1 25:41 - Model routing + why the applied layer wins, even bitter-lesson-pilled 27:45 - GTM is the new moat Checkout Navigators, our new pod on Youtube, Spotify, and Apple Podcasts!

Paul Klein IV

33,542 просмотров • 1 месяц назад

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed
1:22:57

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed

Matt Turck

56,883 просмотров • 1 месяц назад

BREAKING: How a $15+ Trillion AUM Firm is Thinking About the Systemic $10T Rebuild Around AI Inside BlackRock's Fundamental Equities w/ Managing Director & Head of Global Tech, Tony Kim (Tony Kim) The irony of "the trillion dollars of CapEx this year & the $10T over the next 5 years that are coming.. is to move data centimeters & millimeters. That's AI." "Today we're all talking about compute, compute, compute. I think the primacy of memory will become even more important." Tony invests across public & private tech markets, covering semiconductors, memory, data centers, power, software, quantum, & robotics We Cover: › The shift from a software centric world to a compute centric world › The 10-20-30 trillion market cap breakdown, software, Mag 7, and hardware › RAMpocalypse, why memory becomes as important as compute › How BlackRock allocates, 90% in the 3-year AI vortex, 10% on frontier bets converging on 2030 › 140 Chinese robotics companies and the 30-40 IPOs coming this year › Following the "token flow" Special thank you to Brex, MongoDB, & AssemblyAI for helping make this RAISE AI Summit mini-series in Paris, France happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Tony Kim, Head of BlackRock Fundamental Equities Global Technology (01:10) RAISE AI Summit in Paris (03:52) Why Compute now rules everything (05:17) Why old data centers can't survive AI (07:43) Why data centers are ditching Copper for Light (10:44) The shortage nobody saw coming: RAM (15:20) Only three companies control memory (16:03) Tony's Playbook for Investing in the AI Era (18:16) The 20-year lie: "Compute is just a Commodity" (22:38) How Hardware quietly became bigger than Software (27:49) The Investing Rule: Will you still be cool in 5 years? (32:41) 2030: the year every frontier technology bet converges (38:16) Chips were never a Commodity (41:30) Rack design, materials science, & the physical-world renaissance (43:30) Inside the architecture of a Robot's mind (45:03) Why China Is winning the Robotics race (47:37) Why the biggest Robotics market might be companionship (51:34) Speech models are growing faster than anyone expected (53:07) "Token Flow": Tony's Framework for the Future Enterprise (57:56) Are PE roll-ups the next big AI disruption play? (1:00:48) What Could Go Right (& Wrong) in AI This Year (1:03:51) The mentors who shaped Tony Kim's worldview
1:08:44

BREAKING: How a $15+ Trillion AUM Firm is Thinking About the Systemic $10T Rebuild Around AI Inside BlackRock's Fundamental Equities w/ Managing Director & Head of Global Tech, Tony Kim (Tony Kim) The irony of "the trillion dollars of CapEx this year & the $10T over the next 5 years that are coming.. is to move data centimeters & millimeters. That's AI." "Today we're all talking about compute, compute, compute. I think the primacy of memory will become even more important." Tony invests across public & private tech markets, covering semiconductors, memory, data centers, power, software, quantum, & robotics We Cover: › The shift from a software centric world to a compute centric world › The 10-20-30 trillion market cap breakdown, software, Mag 7, and hardware › RAMpocalypse, why memory becomes as important as compute › How BlackRock allocates, 90% in the 3-year AI vortex, 10% on frontier bets converging on 2030 › 140 Chinese robotics companies and the 30-40 IPOs coming this year › Following the "token flow" Special thank you to Brex, MongoDB, & AssemblyAI for helping make this RAISE AI Summit mini-series in Paris, France happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Tony Kim, Head of BlackRock Fundamental Equities Global Technology (01:10) RAISE AI Summit in Paris (03:52) Why Compute now rules everything (05:17) Why old data centers can't survive AI (07:43) Why data centers are ditching Copper for Light (10:44) The shortage nobody saw coming: RAM (15:20) Only three companies control memory (16:03) Tony's Playbook for Investing in the AI Era (18:16) The 20-year lie: "Compute is just a Commodity" (22:38) How Hardware quietly became bigger than Software (27:49) The Investing Rule: Will you still be cool in 5 years? (32:41) 2030: the year every frontier technology bet converges (38:16) Chips were never a Commodity (41:30) Rack design, materials science, & the physical-world renaissance (43:30) Inside the architecture of a Robot's mind (45:03) Why China Is winning the Robotics race (47:37) Why the biggest Robotics market might be companionship (51:34) Speech models are growing faster than anyone expected (53:07) "Token Flow": Tony's Framework for the Future Enterprise (57:56) Are PE roll-ups the next big AI disruption play? (1:00:48) What Could Go Right (& Wrong) in AI This Year (1:03:51) The mentors who shaped Tony Kim's worldview

Molly O’Shea

341,794 просмотров • 1 месяц назад

"There may be some neuro-symbolic hybrid much better than we have right now that smoothly transitions between data in a corpus and abstract representations. The kinds of stuff you guys are doing are compatible with that." — Gary Marcus ⏱️10 highlights: 00:00 Intro: Gary Marcus & Brana Rakic at DKGCon 02:22 Why AI took a wrong turn 04:42 Deep learning vs. deep understanding 06:16 World models: a foundational idea 08:12 The building blocks of trustworthy AI 09:44 Neuro-symbolic AI & abstract relationships 13:04 Safety is an ecosystem - the airplane lesson 15:03 The real risk of AI agents today 18:36 Testing AI like we test new drugs 22:49 Where OpenAI is headed The signal is clear: the path forward runs through world models, abstraction, and an open, accountable ecosystem.
26:06

"There may be some neuro-symbolic hybrid much better than we have right now that smoothly transitions between data in a corpus and abstract representations. The kinds of stuff you guys are doing are compatible with that." — Gary Marcus ⏱️10 highlights: 00:00 Intro: Gary Marcus & Brana Rakic at DKGCon 02:22 Why AI took a wrong turn 04:42 Deep learning vs. deep understanding 06:16 World models: a foundational idea 08:12 The building blocks of trustworthy AI 09:44 Neuro-symbolic AI & abstract relationships 13:04 Safety is an ecosystem - the airplane lesson 15:03 The real risk of AI agents today 18:36 Testing AI like we test new drugs 22:49 Where OpenAI is headed The signal is clear: the path forward runs through world models, abstraction, and an open, accountable ecosystem.

OriginTrail

227,307 просмотров • 2 месяцев назад

Brex co-founder and CEO Pedro Franceschi believes most people still underestimate how much AI will change the way companies are built. AI isn't just another tool, it's a new foundation for building products, teams, and companies. In this episode of Lightcone Podcast, Pedro Franceschi explains why we're only months into a platform shift as significant as the invention of electricity, why the CEO needs to be the chief AI officer, and why founders should rethink what's possible when intelligence is available on demand. 01:13 – How Pedro Became AI-Pilled 04:08 – The Electricity Analogy 05:21 – Free the Claw 06:56 – Making AI Safe for Enterprise 10:57 – Why Most Companies Are Behind 13:09 – AI Teammates, Not Chatbots 14:22 – The Case for Tokenmaxxing 18:24 – The Company of One 20:54 – The One Thing AI Can't Replace 28:06 – Building Customer World Models 32:58 – Rebuilding Brex Around AI 39:02 – The CEO Must Be the Chief AI Officer 43:50 – Building Company AGI 51:43 – Why We're Still So Early
54:06

Brex co-founder and CEO Pedro Franceschi believes most people still underestimate how much AI will change the way companies are built. AI isn't just another tool, it's a new foundation for building products, teams, and companies. In this episode of Lightcone Podcast, Pedro Franceschi explains why we're only months into a platform shift as significant as the invention of electricity, why the CEO needs to be the chief AI officer, and why founders should rethink what's possible when intelligence is available on demand. 01:13 – How Pedro Became AI-Pilled 04:08 – The Electricity Analogy 05:21 – Free the Claw 06:56 – Making AI Safe for Enterprise 10:57 – Why Most Companies Are Behind 13:09 – AI Teammates, Not Chatbots 14:22 – The Case for Tokenmaxxing 18:24 – The Company of One 20:54 – The One Thing AI Can't Replace 28:06 – Building Customer World Models 32:58 – Rebuilding Brex Around AI 39:02 – The CEO Must Be the Chief AI Officer 43:50 – Building Company AGI 51:43 – Why We're Still So Early

Y Combinator

198,105 просмотров • 2 месяцев назад

NEW: Premium Inference 101 The Economics & Infrastructure Behind Running Trillion Parameter Models Rodrigo Liang, CEO & Co-Founder of SambaNova "Inference has arrived. 70-80% of those racks are running inference." "[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable." "With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks." SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW. They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis. The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens. Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic. We Cover: › Why inference will need orders of magnitude more chips than training ever did › The 10kW rack vs the 130kW rack, & why air cooling decides geography › Running a 1T parameter model in one rack at full precision, no quantization › The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone › Revenue per rack, & why inference providers have revenue but no margin › JPMorgan, sovereignty, & the move back to on-prem Filmed at the RAISE Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems (00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation (03:00) The Inference problem nobody saw coming (04:52) SambaNova's chip evolution (07:19) Running a trillion-parameter model on a single rack (11:00) Do $100 billion data centers actually make sense? (14:14) What "premium inference" really means (18:28) Speed is about to become AI's biggest price tag (20:43) Starlink, edge computing, & AI reaching every corner of the planet (24:27) Working alongside NVIDIA & rival chipmakers (27:49) How customers actually measure inference performance (32:07) The biggest bottlenecks in AI's global land grab (35:12) Justifying the billion-dollar AI valuations (37:53) Why SambaNova refuses to build its own cloud (41:03) The "AI sovereignty" debate (43:48) Data privacy fears are driving the return to on-prem AI (47:55) How to actually get ROI out of AI spend (51:16) The one question every business should be asking about AI (56:09) The mentors & lessons behind a 32-year career in chips (58:02) Unveiling SambaNova's newest chip, the SN50
59:56

NEW: Premium Inference 101 The Economics & Infrastructure Behind Running Trillion Parameter Models Rodrigo Liang, CEO & Co-Founder of SambaNova "Inference has arrived. 70-80% of those racks are running inference." "[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable." "With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks." SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW. They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis. The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens. Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic. We Cover: › Why inference will need orders of magnitude more chips than training ever did › The 10kW rack vs the 130kW rack, & why air cooling decides geography › Running a 1T parameter model in one rack at full precision, no quantization › The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone › Revenue per rack, & why inference providers have revenue but no margin › JPMorgan, sovereignty, & the move back to on-prem Filmed at the RAISE Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems (00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation (03:00) The Inference problem nobody saw coming (04:52) SambaNova's chip evolution (07:19) Running a trillion-parameter model on a single rack (11:00) Do $100 billion data centers actually make sense? (14:14) What "premium inference" really means (18:28) Speed is about to become AI's biggest price tag (20:43) Starlink, edge computing, & AI reaching every corner of the planet (24:27) Working alongside NVIDIA & rival chipmakers (27:49) How customers actually measure inference performance (32:07) The biggest bottlenecks in AI's global land grab (35:12) Justifying the billion-dollar AI valuations (37:53) Why SambaNova refuses to build its own cloud (41:03) The "AI sovereignty" debate (43:48) Data privacy fears are driving the return to on-prem AI (47:55) How to actually get ROI out of AI spend (51:16) The one question every business should be asking about AI (56:09) The mentors & lessons behind a 32-year career in chips (58:02) Unveiling SambaNova's newest chip, the SN50

Molly O’Shea

134,960 просмотров • 1 месяц назад

I spoke with Dr. Roman Yampolskiy, the top AI safety expert in the world. He literally coined the term “AI safety” about 15 years ago. Roman explained if AGI is here, what the real threat of AI is to humanity, how many jobs could be lost, why he is bullish on bitcoin, how physical AI & robotics will likely play out, and whether the frontier model labs actually care about AI safety. This was a profound conversation with someone everyone should get to know. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:50 - Have we already hit AGI? 2:51 - Why we can't control superintelligence 5:14 - Could a global ban on superintelligence actually work? 8:45 - Are AI labs really taking safety seriously? 15:52 - Mass job loss & the economics of AGI 22:03 - The surprising link between AI & bitcoin 29:07 - A world of AI abundance, & why scarcity wins 31:45 - AI watermarks, & the death of trust 35:33 - Pros & Cons of physical AI world 38:40 - What would it take to trust humanoid robot? 39:50 - Brain computer interfaces: a good idea? 42:25 - Rise & risks of deapfakes 49:40 - Controlling AI: Governments vs private companies 54:07 - Open source vs closed, & the US vs China AI race 57:34- Is bitcoin the one crypto project built to last? 1:01:40 - Limitations of physical AI: bottlenecks & shortages 1:03:50 - Impact of healthcare with AI 1:06:09 - What would it take to actually declare AGI? 1:09:35 - How the public can help with AI safety
1:10:37

I spoke with Dr. Roman Yampolskiy, the top AI safety expert in the world. He literally coined the term “AI safety” about 15 years ago. Roman explained if AGI is here, what the real threat of AI is to humanity, how many jobs could be lost, why he is bullish on bitcoin, how physical AI & robotics will likely play out, and whether the frontier model labs actually care about AI safety. This was a profound conversation with someone everyone should get to know. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:50 - Have we already hit AGI? 2:51 - Why we can't control superintelligence 5:14 - Could a global ban on superintelligence actually work? 8:45 - Are AI labs really taking safety seriously? 15:52 - Mass job loss & the economics of AGI 22:03 - The surprising link between AI & bitcoin 29:07 - A world of AI abundance, & why scarcity wins 31:45 - AI watermarks, & the death of trust 35:33 - Pros & Cons of physical AI world 38:40 - What would it take to trust humanoid robot? 39:50 - Brain computer interfaces: a good idea? 42:25 - Rise & risks of deapfakes 49:40 - Controlling AI: Governments vs private companies 54:07 - Open source vs closed, & the US vs China AI race 57:34- Is bitcoin the one crypto project built to last? 1:01:40 - Limitations of physical AI: bottlenecks & shortages 1:03:50 - Impact of healthcare with AI 1:06:09 - What would it take to actually declare AGI? 1:09:35 - How the public can help with AI safety

Anthony Pompliano 🌪

105,912 просмотров • 1 день назад

NEW: Dylan Field (Dylan Field), CEO of Figma (NYSE: FIG) How to Escape the "Permanent Underclass of Zero Taste" › Why AI prompts only get you the average › Why the market has design backwards Plus, Founders Fund's Mafia & Thiel Fellowship class behind Figma + Anthropic Recorded at Day 0 of Config 2026, San Francisco We Cover: › Vibe Mathing › Elon: Products vs Logos › AI's trust issue › Agents vs Employees › Engineers coding design › American Enterprise > › IQ ≠ Judgment or taste › What actually is a jailbreak? This was SO much fun!!! Can confirm Config-pilled. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Dylan Field, Co-Founder & CEO at Figma (00:57) Day Zero begins: backstage at Config (01:44) The "Coachella for Design" is real (02:35) Why Dylan started Config back in 2020 (03:47) Why "design is dead" is completely wrong (06:11) How Figma turned engineers into designers (06:54) Inside Config before the crowd arrives (07:55) The Config speakers Dylan can't wait to watch (11:34) Inside the exclusive Pantone Lounge (12:00) The one question every designer gets asked (13:16) Getting killed in every single game of Mafia (15:33) How to escape the permanent underclass of zero taste (19:51) Why execution is cheap but taste is everything (22:25) What actually builds a trusted brand (25:51) How AI agents are rewiring company structure (28:54) The AI safety debate happening right now (31:57) Why deep curiosity is Dylan's secret weapon (34:53) The real lesson from being a Thiel fellow (36:52) Rapid fire: Elad Gil's wildest question (38:04) What people are missing about AI & enterprise software (40:16) Where Figma goes next (41:21) Why Dylan is bullish on SpaceX (42:43) Elon Musk's logo theory (43:26) The mentors who shaped Dylan Field (45:20) Alan Kay, VR, and the future of human-computer interaction
47:33

NEW: Dylan Field (Dylan Field), CEO of Figma (NYSE: FIG) How to Escape the "Permanent Underclass of Zero Taste" › Why AI prompts only get you the average › Why the market has design backwards Plus, Founders Fund's Mafia & Thiel Fellowship class behind Figma + Anthropic Recorded at Day 0 of Config 2026, San Francisco We Cover: › Vibe Mathing › Elon: Products vs Logos › AI's trust issue › Agents vs Employees › Engineers coding design › American Enterprise > › IQ ≠ Judgment or taste › What actually is a jailbreak? This was SO much fun!!! Can confirm Config-pilled. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Dylan Field, Co-Founder & CEO at Figma (00:57) Day Zero begins: backstage at Config (01:44) The "Coachella for Design" is real (02:35) Why Dylan started Config back in 2020 (03:47) Why "design is dead" is completely wrong (06:11) How Figma turned engineers into designers (06:54) Inside Config before the crowd arrives (07:55) The Config speakers Dylan can't wait to watch (11:34) Inside the exclusive Pantone Lounge (12:00) The one question every designer gets asked (13:16) Getting killed in every single game of Mafia (15:33) How to escape the permanent underclass of zero taste (19:51) Why execution is cheap but taste is everything (22:25) What actually builds a trusted brand (25:51) How AI agents are rewiring company structure (28:54) The AI safety debate happening right now (31:57) Why deep curiosity is Dylan's secret weapon (34:53) The real lesson from being a Thiel fellow (36:52) Rapid fire: Elad Gil's wildest question (38:04) What people are missing about AI & enterprise software (40:16) Where Figma goes next (41:21) Why Dylan is bullish on SpaceX (42:43) Elon Musk's logo theory (43:26) The mentors who shaped Dylan Field (45:20) Alan Kay, VR, and the future of human-computer interaction

Molly O’Shea

428,378 просмотров • 1 месяц назад