Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

This week, Satya Nadella gave Dwarkesh Patel and Dylan Patel from SemiAnalysis an exclusive look at Microsoft’s Fairwater 2, where a single building is more powerful than every other AI datacenter. 💰Dylan estimates AI CapEx will hit half a trillion dollars next year, with labs spending billions to secure...

labelbox's profile picture

Labelbox

3,527 subscribers

175,689 Aufrufe • vor 9 Monaten •via X (Twitter)

Wissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

.Satya Nadella gave me and Dylan Patel an exclusive tour of Fairwater 2, the most powerful AI datacenter in the world. We then chatted through Satya's vision for Microsoft in a world with AGI. 0:00:00 - Fairwater 2 0:04:15 - Business models for AGI 0:13:42 - Copilot 0:20:56 - Whose margins will expand most? 0:37:12 - MAI 0:48:42 - The hyperscale business 1:03:39 - In-house chip & OpenAI partnership 1:10:30 - The CAPEX explosion 1:16:01 - Will the world trust US companies to lead AI? Look up Dwarkesh Podcast on Youtube, Apple Podcasts or Spotify to tune in.
1:28:41

.Satya Nadella gave me and Dylan Patel an exclusive tour of Fairwater 2, the most powerful AI datacenter in the world. We then chatted through Satya's vision for Microsoft in a world with AGI. 0:00:00 - Fairwater 2 0:04:15 - Business models for AGI 0:13:42 - Copilot 0:20:56 - Whose margins will expand most? 0:37:12 - MAI 0:48:42 - The hyperscale business 1:03:39 - In-house chip & OpenAI partnership 1:10:30 - The CAPEX explosion 1:16:01 - Will the world trust US companies to lead AI? Look up Dwarkesh Podcast on Youtube, Apple Podcasts or Spotify to tune in.

Dwarkesh Patel

1,895,952 Aufrufe • vor 9 Monaten

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and kingston kuan spent years building large-scale infrastructure at Jane Street.
2:17

Today we're launching Hebbian Robotics (YC S26) Hebbian Robotics (YC S26) . We build APIs for evaluating data quality, without training a robotics model. Data vendors use our API to search and analyze Physical AI data at scale, getting on-demand quality signals and metrics without managing infrastructure. High-quality data is the bottleneck to the next frontier of intelligence in robotics. Buyers want data they can trust. Data providers are focused on operational excellence. They don't have the bandwidth to understand the quality of their data at scale. We're fixing this. Vendors use our APIs to understand the impact of their decisions on their data quality: - Buyers demanding per-collector hour limits? Prove compliance while we stay ahead of the workarounds. - Changed your collection SOPs? Monitor for quality drift and regression. - Suspect duplicate samples? Verify with a single API call. Before Hebbian, I trained AI models for industrial cleaning robots, and kingston kuan spent years building large-scale infrastructure at Jane Street.

Brandon Ong

165,737 Aufrufe • vor 23 Tagen

CARV envisions a new form of evolution for Artificial General Intelligence (AGI): one driven by the aggregation, sharing, and refinement of high-quality on-chain and off-chain data. To do that, CARV is building an AI chain ecosystem to enable data sovereignty at scale: • CARV SVM Chain, an AI agentic infrastructure that extends SVM's capabilities onto Ethereum. • D.A.T.A. Framework, powered by CARV ID, delivers high-quality on-chain and off-chain data, enriched with tags and metrics, enabling AI agents to learn, share, and evolve dynamically. • CARV Labs accelerates ecosystem growth in and beyond CARV’s ecosystem, spearheading innovation in the AI agentic infrastructure enabling data sovereignty with incubation, grants, and infrastructure. You build?
1:08

CARV envisions a new form of evolution for Artificial General Intelligence (AGI): one driven by the aggregation, sharing, and refinement of high-quality on-chain and off-chain data. To do that, CARV is building an AI chain ecosystem to enable data sovereignty at scale: • CARV SVM Chain, an AI agentic infrastructure that extends SVM's capabilities onto Ethereum. • D.A.T.A. Framework, powered by CARV ID, delivers high-quality on-chain and off-chain data, enriched with tags and metrics, enabling AI agents to learn, share, and evolve dynamically. • CARV Labs accelerates ecosystem growth in and beyond CARV’s ecosystem, spearheading innovation in the AI agentic infrastructure enabling data sovereignty with incubation, grants, and infrastructure. You build?

CARV

274,756 Aufrufe • vor 1 Jahr

Is your AI infrastructure ready for the agentic era? At Cisco Live, I connected with Gordon Macintosh and Dale Tucker from Equinix, Inc. to explore how the Secure AI Factory ecosystem is building the physical and interconnection foundations that make agentic AI at scale possible. Three thing leaders are still overlooking: → Distributed infrastructure. Production AI = multiple agents, tools, data sources, and policies → Data gravity is real. Sovereignty, regulation, and sensitivity shape where AI can run. → Last mile connectivity is where strategies stall. Secure, predictable, low-latency connections aren't optional at production scale. The Cisco + NVIDIA + Equinix, Inc. reference architecture is one of the most concrete answers I've seen with Equinix as the neutral landing zone across 280+ data centers worldwide. As Dale put it: "Last mile connectivity is where AI strategy can either accelerate or stall." The companies that win the next phase of AI will be the ones who got the infrastructure and the ecosystem right. Watch our full conversation here: #CiscoLive #SecureAIFactory #EquinixPartner #CiscoPartner
1:06

Is your AI infrastructure ready for the agentic era? At Cisco Live, I connected with Gordon Macintosh and Dale Tucker from Equinix, Inc. to explore how the Secure AI Factory ecosystem is building the physical and interconnection foundations that make agentic AI at scale possible. Three thing leaders are still overlooking: → Distributed infrastructure. Production AI = multiple agents, tools, data sources, and policies → Data gravity is real. Sovereignty, regulation, and sensitivity shape where AI can run. → Last mile connectivity is where strategies stall. Secure, predictable, low-latency connections aren't optional at production scale. The Cisco + NVIDIA + Equinix, Inc. reference architecture is one of the most concrete answers I've seen with Equinix as the neutral landing zone across 280+ data centers worldwide. As Dale put it: "Last mile connectivity is where AI strategy can either accelerate or stall." The companies that win the next phase of AI will be the ones who got the infrastructure and the ecosystem right. Watch our full conversation here: #CiscoLive #SecureAIFactory #EquinixPartner #CiscoPartner

Helen Yu

11,558 Aufrufe • vor 2 Monaten

LLMs had the internet. Robotics doesn’t, which is why we’re bringing the Scale AI data engine into the physical world. Robotics won’t be solved by blindly collecting 100x more demos. Raw trajectories need structure (intent, subgoals, failures, recoveries, edge cases, quality signals) or you just average mistakes. At Scale Labs, we’re building that data flywheel, mapping the right data to the right capabilities and scaling what works globally !
1:30

LLMs had the internet. Robotics doesn’t, which is why we’re bringing the Scale AI data engine into the physical world. Robotics won’t be solved by blindly collecting 100x more demos. Raw trajectories need structure (intent, subgoals, failures, recoveries, edge cases, quality signals) or you just average mistakes. At Scale Labs, we’re building that data flywheel, mapping the right data to the right capabilities and scaling what works globally !

jade

15,262 Aufrufe • vor 4 Monaten

🚀 Musk, Dwarkesh & Collison sat down over Guinness to map out AI’s biggest roadblocks - solar compute, robotics, power bottlenecks & more. Labelbox joins the convo to show how top teams train AI that scales. Watch now.
1:11

🚀 Musk, Dwarkesh & Collison sat down over Guinness to map out AI’s biggest roadblocks - solar compute, robotics, power bottlenecks & more. Labelbox joins the convo to show how top teams train AI that scales. Watch now.

Labelbox

1,938,776 Aufrufe • vor 6 Monaten

AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀
0:49

AI is transforming the way businesses operate 🤖. It’s no longer just a tool, it powers decision-making, efficiency, and growth. From automating tasks to predicting trends and uncovering insights, AI is becoming the backbone of modern business. But AI isn’t magic. It needs massive amounts of high-quality data 📊. Companies like Scale AI and other Web2 giants thrive because they control the pipelines of this data, turning it into a huge competitive advantage. The problem is most of this data is locked in centralized systems 🔒, expensive, and often inaccessible for smaller companies or innovators. That’s where OORT | The Data Cloud for Decentralized AI comes in 🌐✨. By building decentralized storage and AI infrastructure, OORTECH makes data open, secure, and accessible to everyone. It’s a foundation for the next generation of AI-powered businesses, giving all innovators the chance to compete and create 🚀

Sky Wee

67,557 Aufrufe • vor 11 Monaten

What if the question isn’t whether to use AI, but how fast you can learn to work with it? In this special episode recorded live at Webex One, I talk with Jeetu Patel (Jeetu Patel), President and Chief Product Officer at Cisco, about the mindset shift every company, and every professional, must make to thrive in the AI era. From rethinking work culture to designing products for both humans and AI agents, Jeetu shares a thoughtful, optimistic vision for the future, one rooted in curiosity, responsibility, and human creativity. “AI won’t take your job. But someone who uses AI better than you might.” An honest, energizing look at how to lead and learn in an AI-first world. 1:08 Embracing AI In The Workforce 5:17 AI Is More Than A Productivity Tool 8:35 Generational Differences In AI Use 10:49 The Future of Integrating AI Agents 13:44 AI As A "Given" 17:08 Speed of Accumulating Knowledge 19:00 Ethical AI Use 21:16 What The Industry Gets Wrong About AI
23:48

What if the question isn’t whether to use AI, but how fast you can learn to work with it? In this special episode recorded live at Webex One, I talk with Jeetu Patel (Jeetu Patel), President and Chief Product Officer at Cisco, about the mindset shift every company, and every professional, must make to thrive in the AI era. From rethinking work culture to designing products for both humans and AI agents, Jeetu shares a thoughtful, optimistic vision for the future, one rooted in curiosity, responsibility, and human creativity. “AI won’t take your job. But someone who uses AI better than you might.” An honest, energizing look at how to lead and learn in an AI-first world. 1:08 Embracing AI In The Workforce 5:17 AI Is More Than A Productivity Tool 8:35 Generational Differences In AI Use 10:49 The Future of Integrating AI Agents 13:44 AI As A "Given" 17:08 Speed of Accumulating Knowledge 19:00 Ethical AI Use 21:16 What The Industry Gets Wrong About AI

Carolina Milanesi

37,076 Aufrufe • vor 10 Monaten

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

14,864 Aufrufe • vor 2 Monaten

BEHIND THE CAPEX BOOM: In an interview with CNBC’s Michael Santoli this week, we explore how to think about the next leg of the AI capex cycle. There’s been a world awash with liquidity for the first half of 2026, and conditions are set to tighten. But this doesn’t need to overly upset the AI buildout. What it does mean, though, is that capex will likely be more expensive. And there are a few reasons for that: 📈 Yields have been elevated and the 10-year is close to 4.8%, a level that’s tended to dent risk appetite. 💰With a higher-for-longer environment plus spreads beginning to widen, this simply increases the cost of borrowing that will support the AI buildout for companies that have increased risk profiles due to pressure on free cash flow. ⚡️The buildout will require more investment in communities. Power, as an example, where there is a real need for more spend. One consideration: Will hyperscalers need to help subsidize the grid in regions with constrained capacity? This is a building idea, top of mind for infrastructure investors. 🧾 The price tag is rising partially because the bill is getting longer. Add supply chain constraints, higher energy costs and a memory shortage and you easily see why a more than $800 billion buildout this year can turn closer to $1.5 trillion for next year. Full conversation in the link. Thank you to CNBCOvertime for having me:
0:55

BEHIND THE CAPEX BOOM: In an interview with CNBC’s Michael Santoli this week, we explore how to think about the next leg of the AI capex cycle. There’s been a world awash with liquidity for the first half of 2026, and conditions are set to tighten. But this doesn’t need to overly upset the AI buildout. What it does mean, though, is that capex will likely be more expensive. And there are a few reasons for that: 📈 Yields have been elevated and the 10-year is close to 4.8%, a level that’s tended to dent risk appetite. 💰With a higher-for-longer environment plus spreads beginning to widen, this simply increases the cost of borrowing that will support the AI buildout for companies that have increased risk profiles due to pressure on free cash flow. ⚡️The buildout will require more investment in communities. Power, as an example, where there is a real need for more spend. One consideration: Will hyperscalers need to help subsidize the grid in regions with constrained capacity? This is a building idea, top of mind for infrastructure investors. 🧾 The price tag is rising partially because the bill is getting longer. Add supply chain constraints, higher energy costs and a memory shortage and you easily see why a more than $800 billion buildout this year can turn closer to $1.5 trillion for next year. Full conversation in the link. Thank you to CNBCOvertime for having me:

Sonali Basak

15,168 Aufrufe • vor 20 Tagen

We recently invited Dwarkesh Patel to stop by our SF robotics lab. World-class podcaster, rookie robotics intern.
1:14

We recently invited Dwarkesh Patel to stop by our SF robotics lab. World-class podcaster, rookie robotics intern.

Labelbox

1,004,219 Aufrufe • vor 11 Monaten

AI INFRASTRUCTURE/ENERGY DEMAND STOCKS TO KEEP ON WATCH FOR 2026 Ex-CEO $GOOGL Eric Schmidt on energy demand driven by AI: "We need energy and the numbers are profound. What we need from you is energy in all forms. People are planning 10 gigawatt data centers" $IREN: owns and develops vertically integrated data centers for hyperscalers $CIFR: large power footprint, 3.4 GW pipeline for AI workloads + HPC $WULF: high capacity, low cost data center expansion across Texas and New York $NBIS: AI compute and cloud services translating energy into revenue $CLSK: America’s Bitcoin Miner transitioning to AI infrastructure with a massive power footprint across 30+ data center sites $BITF: owns and operates power sites positioned for AI workloads, yet to sign with a hyperscaler but could come this year $GLXY: multi-gigawatt data center infrastructure at scale
0:47

AI INFRASTRUCTURE/ENERGY DEMAND STOCKS TO KEEP ON WATCH FOR 2026 Ex-CEO $GOOGL Eric Schmidt on energy demand driven by AI: "We need energy and the numbers are profound. What we need from you is energy in all forms. People are planning 10 gigawatt data centers" $IREN: owns and develops vertically integrated data centers for hyperscalers $CIFR: large power footprint, 3.4 GW pipeline for AI workloads + HPC $WULF: high capacity, low cost data center expansion across Texas and New York $NBIS: AI compute and cloud services translating energy into revenue $CLSK: America’s Bitcoin Miner transitioning to AI infrastructure with a massive power footprint across 30+ data center sites $BITF: owns and operates power sites positioned for AI workloads, yet to sign with a hyperscaler but could come this year $GLXY: multi-gigawatt data center infrastructure at scale

Small Cap Snipa

116,034 Aufrufe • vor 8 Monaten

Thrilled to be featured in Dwarkesh’s latest episode with Richard Sutton, widely regarded as the father of reinforcement learning and 2024 Turing Award winner. As Richard explains, we’re entering the Era of Experience, where training AI means creating environments that capture the complexity, unpredictability, and subtle rules of real life. What we’ve learned: building these environments takes deep expertise in data, workflows, and nuances. At Labelbox, we’re proud to help teams tackle today’s RL challenges while also shaping the next era of AI, turning ambitious projects into working systems. (Learn more in the comments)
1:08

Thrilled to be featured in Dwarkesh’s latest episode with Richard Sutton, widely regarded as the father of reinforcement learning and 2024 Turing Award winner. As Richard explains, we’re entering the Era of Experience, where training AI means creating environments that capture the complexity, unpredictability, and subtle rules of real life. What we’ve learned: building these environments takes deep expertise in data, workflows, and nuances. At Labelbox, we’re proud to help teams tackle today’s RL challenges while also shaping the next era of AI, turning ambitious projects into working systems. (Learn more in the comments)

Labelbox

293,695 Aufrufe • vor 11 Monaten

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.
1:47

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.

Aaron Levie

139,591 Aufrufe • vor 1 Jahr

Palantir is playing an integral role in the AI Revolution, and Akshay Krishnaswamy summarized it best⚡️ “Our conviction here at Palantir is that really it constitutes a new category in the AI stack...Of course there's raw energy. You then have chips, you have data centers, you have the frontier models that are increasingly commoditized and available but increasingly powerful as well. And then there's everything you need to get from that to differentiated outcomes, products, and services. And we are maniacally focused on providing this AI infrastructure for developer teams, for enterprises, for everybody looking to achieve value with AI.”
0:37

Palantir is playing an integral role in the AI Revolution, and Akshay Krishnaswamy summarized it best⚡️ “Our conviction here at Palantir is that really it constitutes a new category in the AI stack...Of course there's raw energy. You then have chips, you have data centers, you have the frontier models that are increasingly commoditized and available but increasingly powerful as well. And then there's everything you need to get from that to differentiated outcomes, products, and services. And we are maniacally focused on providing this AI infrastructure for developer teams, for enterprises, for everybody looking to achieve value with AI.”

Eliano A. Younes

11,513 Aufrufe • vor 1 Jahr

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.
0:32

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

Cristiano R. Amon

15,783 Aufrufe • vor 2 Monaten

I think the fact is there are only going to be four or five of these companies that are able to raise the billions required to build the infrastructure to support these models. It's a global competition, and particularly against the Chinese right now because they're trying to beat us on AI, because the country with the best AI is gonna win all the wars. That's what's gonna happen. I think at the end of the day you want diversification in your portfolio, not just one stack. For me, I'm playing the picks and shovels. I'm building energy because you need energy for this stuff, and I'm building data centers. Now, there are different things, but right now we need more electrical power in this country beyond just data centers. We just need power because we got a booming economy.
2:33

I think the fact is there are only going to be four or five of these companies that are able to raise the billions required to build the infrastructure to support these models. It's a global competition, and particularly against the Chinese right now because they're trying to beat us on AI, because the country with the best AI is gonna win all the wars. That's what's gonna happen. I think at the end of the day you want diversification in your portfolio, not just one stack. For me, I'm playing the picks and shovels. I'm building energy because you need energy for this stuff, and I'm building data centers. Now, there are different things, but right now we need more electrical power in this country beyond just data centers. We just need power because we got a booming economy.

Kevin O'Leary aka Mr. Wonderful

13,143 Aufrufe • vor 3 Monaten

.Ilya Sutskever on why OpenAI gave up on robotics: "Progress comes from the combination of compute and data. There was no path to data from robotics. I'd say that now it is possible to create a path forward, but one needs to really commit. You could imagine a gradual path of improvement where you build more robots, they do more things, you collect more data and so on. But you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them. It's not the same as software at all."
1:57

.Ilya Sutskever on why OpenAI gave up on robotics: "Progress comes from the combination of compute and data. There was no path to data from robotics. I'd say that now it is possible to create a path forward, but one needs to really commit. You could imagine a gradual path of improvement where you build more robots, they do more things, you collect more data and so on. But you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them. It's not the same as software at all."

Dwarkesh Patel

325,893 Aufrufe • vor 2 Jahren

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."
2:08

Interesting that in three of my recent conversations with Krishna at Anthropic, Dylan Patel, and Gavin Baker, each said that frontier tokens are capturing the majority of the economic value. Gavin: "An overwhelming amount of the economic returns to AI at the model layer have been at the frontier. That's surprising to me, and I think it's been surprising to a lot of people. This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor." Krishna: "We think the returns to frontier intelligence are extremely high. Customers invest really heavily in more tokens with the newer models. The ones at the frontier clearly are capturing this economic value, driving meaningful ROI for customers. The returns to frontier intelligence are not slowing down." Dylan: "No one gives a crap about GPT-4 class models. They want the frontier because the frontier lets them create the economically valuable things."

Patrick OShaughnessy

121,110 Aufrufe • vor 3 Monaten