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How can decentralized content networks make AI safer? JoeVezz (LunarCrush), Ari Jónsson (Videntifier) & Tomaz L explain why verifiable knowledge is key to building trustworthy AI at DKGcon 2025. 03:20 - AI productivity benefits today 08:30 - Long-term societal impact of AI 19:00 - Protecting identity and content online

OriginTrail

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59,708 просмотров • 8 месяцев назад •via X (Twitter)

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Ethernet pioneer Dr. Bob Metcalfe joined Brana Rakic at DKGcon 2025 to discuss how blockchains, LLMs and the DKG converge to power trusted intelligence networks. 00:00 – AI & knowledge graph convergence 04:35 – Internet pathologies 13:40 – Decentralized, user-defined filters 23:10 – Why interoperability matters

Ethernet pioneer Dr. Bob Metcalfe joined Brana Rakic at DKGcon 2025 to discuss how blockchains, LLMs and the DKG converge to power trusted intelligence networks. 00:00 – AI & knowledge graph convergence 04:35 – Internet pathologies 13:40 – Decentralized, user-defined filters 23:10 – Why interoperability matters

OriginTrail

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Dr. Fei-Fei Li (Fei-Fei Li), the Godmother of AI, on the origin of computer vision and why spatial intelligence is the next frontier. At AI Startup School in San Francisco. 01:00 - Origins of ImageNet 02:15 - The Dream to Make Machines See 03:28 - A paradigm shift in AI 05:50 - AlexNet and Deep Learning 08:00 - Evolving Computer Vision 12:20 - Building World Labs 13:00 - The next frontier in AI 14:20 - Why Spatial Intelligence Is Harder Than Language 18:40 - Technical Barriers for Vision-Based AI 20:00 - AI is more than LLMs 25:50 - Fei Fei’s Journey 29:30 - Mentoring some of the legends of AI 33:00 - Audience Q&A

Dr. Fei-Fei Li (Fei-Fei Li), the Godmother of AI, on the origin of computer vision and why spatial intelligence is the next frontier. At AI Startup School in San Francisco. 01:00 - Origins of ImageNet 02:15 - The Dream to Make Machines See 03:28 - A paradigm shift in AI 05:50 - AlexNet and Deep Learning 08:00 - Evolving Computer Vision 12:20 - Building World Labs 13:00 - The next frontier in AI 14:20 - Why Spatial Intelligence Is Harder Than Language 18:40 - Technical Barriers for Vision-Based AI 20:00 - AI is more than LLMs 25:50 - Fei Fei’s Journey 29:30 - Mentoring some of the legends of AI 33:00 - Audience Q&A

Y Combinator

194,705 просмотров • 1 год назад

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training

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EigenCloud

62,099 просмотров • 11 месяцев назад

Today on MCG $AI | LONG() w/ Nate Key Highlights: 00:00 – Intro: Natan from long joins for an update 05:50 – Natan's background: CS roots, mechanism design, building Long 07:20 – The numbers: 130M+ RWA volume, ~20% of Robinhood RWA activity 10:20 – The big picture: bridging brokerage assets on-chain 15:00 – AI tokenomics: fees buy back and lock AI 19:50 – Scale check: AI holds ~20% of Nvidia token supply 23:30 – The vision: AI as the "Trump coin of RWA" 25:20 – BIGGEST UPDATE: Long x Lighter tokenized leveraged positions 30:40 – Why it's a moat: ZK-proof router, near-impossible to fork 36:50 – Live reaction: chart pumps as update drops 39:00 – Mechanics Q&A: leverage, liquidation, collateral

Today on MCG $AI | LONG() w/ Nate Key Highlights: 00:00 – Intro: Natan from long joins for an update 05:50 – Natan's background: CS roots, mechanism design, building Long 07:20 – The numbers: 130M+ RWA volume, ~20% of Robinhood RWA activity 10:20 – The big picture: bridging brokerage assets on-chain 15:00 – AI tokenomics: fees buy back and lock AI 19:50 – Scale check: AI holds ~20% of Nvidia token supply 23:30 – The vision: AI as the "Trump coin of RWA" 25:20 – BIGGEST UPDATE: Long x Lighter tokenized leveraged positions 30:40 – Why it's a moat: ZK-proof router, near-impossible to fork 36:50 – Live reaction: chart pumps as update drops 39:00 – Mechanics Q&A: leverage, liquidation, collateral

MCG

30,439 просмотров • 3 дней назад

Most people experience AI through apps. Those apps run on infrastructure owned by a handful of companies. Ben Fielding and Harry Grieve of gensyn on why that needs to change, and what it's going to take to rebuild AI as crypto-native open infrastructure. 00:00 – Intro 00:29 – Misconceptions about AI infrastructure 01:20 – Why centralization in AI is a deeper problem than people realize 04:19 – Why AI needs crypto 05:51 – How AI models are trained 08:15 – The rise of autonomous AI agents with onchain identities 10:37 – Lightning round

Most people experience AI through apps. Those apps run on infrastructure owned by a handful of companies. Ben Fielding and Harry Grieve of gensyn on why that needs to change, and what it's going to take to rebuild AI as crypto-native open infrastructure. 00:00 – Intro 00:29 – Misconceptions about AI infrastructure 01:20 – Why centralization in AI is a deeper problem than people realize 04:19 – Why AI needs crypto 05:51 – How AI models are trained 08:15 – The rise of autonomous AI agents with onchain identities 10:37 – Lightning round

a16z crypto

24,252 просмотров • 4 месяцев назад

The accidental success of ai16z has catapulted AI agents and their transformational promise into the spotlight. .Shaw (spirit/acc), founder of Eliza Labs, talks about: 🔗 How the Eliza framework brings AI agents to every blockchain 📈 How ai16z is rethinking DAOs and decentralized investing 🌍 The societal shifts AI could bring—and who will control its value Timestamps: 00:00 Intro 03:01 Shaw’s inspiration for launching ai16z 08:45 How AI agents are evolving beyond simple tasks 10:28 The Eliza framework and its open-source impact 13:22 Can AI agents excel at trading? 21:19 Why Eliza works seamlessly across multiple blockchains 24:08 The story behind launching Marc Andreessen as an AI character 35:18 Plans for ai16z’s layer 1 and its future launchpad 40:23 Transforming AI agents into more than just “reply bots” 43:26 Shaw’s vision for AI’s role in shaping human society 49:26 Crypto News Recap Thank you to our sponsors! Kelp Polkadot

The accidental success of ai16z has catapulted AI agents and their transformational promise into the spotlight. .Shaw (spirit/acc), founder of Eliza Labs, talks about: 🔗 How the Eliza framework brings AI agents to every blockchain 📈 How ai16z is rethinking DAOs and decentralized investing 🌍 The societal shifts AI could bring—and who will control its value Timestamps: 00:00 Intro 03:01 Shaw’s inspiration for launching ai16z 08:45 How AI agents are evolving beyond simple tasks 10:28 The Eliza framework and its open-source impact 13:22 Can AI agents excel at trading? 21:19 Why Eliza works seamlessly across multiple blockchains 24:08 The story behind launching Marc Andreessen as an AI character 35:18 Plans for ai16z’s layer 1 and its future launchpad 40:23 Transforming AI agents into more than just “reply bots” 43:26 Shaw’s vision for AI’s role in shaping human society 49:26 Crypto News Recap Thank you to our sponsors! Kelp Polkadot

Laura Shin

216,192 просмотров • 1 год назад

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation

TAO τalk 🥩🦍

24,929 просмотров • 1 год назад

During EthCC - Ethereum Community Conference had the pleasure to sit down with Ram from OpenLedger and discuss why building decentralized AI is key. Some of my highlights: → From Bitcoin to AI protocols → Solving data ownership with attribution → How enterprises move into Web3 → Rewarding contributors in AI model training → The future of AI + Blockchain convergence ⏱️ Timestamps 0:00 – Intro & Ram’s journey 3:00 – Origins 4:30 – Why attribution matters 6:00 – Enterprise Web3 lessons 8:15 – ChatGPT moment & protocols 10:00 – Closing thoughts

During EthCC - Ethereum Community Conference had the pleasure to sit down with Ram from OpenLedger and discuss why building decentralized AI is key. Some of my highlights: → From Bitcoin to AI protocols → Solving data ownership with attribution → How enterprises move into Web3 → Rewarding contributors in AI model training → The future of AI + Blockchain convergence ⏱️ Timestamps 0:00 – Intro & Ram’s journey 3:00 – Origins 4:30 – Why attribution matters 6:00 – Enterprise Web3 lessons 8:15 – ChatGPT moment & protocols 10:00 – Closing thoughts

Francesco Andreoli ᵍᵐ

75,306 просмотров • 11 месяцев назад

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Matt Turck

33,195 просмотров • 3 месяцев назад

New Professionally Curious 🧠 w/ Joanna Zeng @ SOON (🔴,💊)📍🇺🇸🗽 (Mainnet Arc) + Michael Heinrich (Ø,G), Founder & CEO of 0G Labs (Home of Infinite AI) From the 2016 ICO boom → launching 0G → building decentralized AI, Michael shares what it takes to scale high-performance systems, navigate post-TGE leadership, and turn AI into a public good through modular blockchain design. SOON takeaway: The future of high-performance crypto isn’t just speed — it’s interoperability and trust. Like 0G, SOON is building the infrastructure where AI, blockchain, and user experience converge to power the next wave of decentralized systems. Key Highlights: • Michael’s journey from early crypto to founding 0G • How modular blockchains unlock scalable decentralized AI • Collaboration across ecosystems drives innovation • Privacy layers as the foundation for trustworthy AI agents • TGE as a starting point — not the finish line • Long-term building in the era of AI + crypto convergence ⏱️ Timestamps 00:00 Michael’s crypto journey 05:25 Founding 0G 10:31 Building modular AI infrastructure 13:35 Collaborating with the Solana ecosystem 17:52 Life and leadership post-TGE 24:51 Future priorities & ecosystem growth 28:38 Privacy layers & AI agents 31:00 Building for the long term Listen to the full episode below 👇

New Professionally Curious 🧠 w/ Joanna Zeng @ SOON (🔴,💊)📍🇺🇸🗽 (Mainnet Arc) + Michael Heinrich (Ø,G), Founder & CEO of 0G Labs (Home of Infinite AI) From the 2016 ICO boom → launching 0G → building decentralized AI, Michael shares what it takes to scale high-performance systems, navigate post-TGE leadership, and turn AI into a public good through modular blockchain design. SOON takeaway: The future of high-performance crypto isn’t just speed — it’s interoperability and trust. Like 0G, SOON is building the infrastructure where AI, blockchain, and user experience converge to power the next wave of decentralized systems. Key Highlights: • Michael’s journey from early crypto to founding 0G • How modular blockchains unlock scalable decentralized AI • Collaboration across ecosystems drives innovation • Privacy layers as the foundation for trustworthy AI agents • TGE as a starting point — not the finish line • Long-term building in the era of AI + crypto convergence ⏱️ Timestamps 00:00 Michael’s crypto journey 05:25 Founding 0G 10:31 Building modular AI infrastructure 13:35 Collaborating with the Solana ecosystem 17:52 Life and leadership post-TGE 24:51 Future priorities & ecosystem growth 28:38 Privacy layers & AI agents 31:00 Building for the long term Listen to the full episode below 👇

SOON - Solana Optimistic Network (Mainnet Arc)

70,794 просмотров • 9 месяцев назад

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing

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MR SHIFT 🦁

41,057 просмотров • 8 месяцев назад

Why Most Service Departments Leak Calls – And What Actually Stops It | Pre-NADA AI #5 Stream the full episode now, hosted by Sam D'Arc: 02:27 What are Kissee Ford's operational challenges? 08:45 How can AI solve service call problems? 14:46 What is "The Flai Solution"? 20:45 What were the early AI successes? 21:53 How were vendor issues solved? 23:57 How did "Amanda" impact operations? 26:00 How was AI integrated at the dealership? 32:03 What are future AI plans?

Why Most Service Departments Leak Calls – And What Actually Stops It | Pre-NADA AI #5 Stream the full episode now, hosted by Sam D'Arc: 02:27 What are Kissee Ford's operational challenges? 08:45 How can AI solve service call problems? 14:46 What is "The Flai Solution"? 20:45 What were the early AI successes? 21:53 How were vendor issues solved? 23:57 How did "Amanda" impact operations? 26:00 How was AI integrated at the dealership? 32:03 What are future AI plans?

Car Dealership Guy

11,530 просмотров • 7 месяцев назад

EPISODE 150: How AI Is Transforming Diligence, Decision-Making & the Future of Investing Joe Lonsdale sits down with John Melas-Kyriazi, co-founder & CEO of Standard Metrics (00:00) Episode intro (02:10) Academia to investor and founder (08:00) The pain point that led to Standard Metrics (12:10) How AI is transforming diligence, data collection & VC (14:20) AI as a technical copilot (17:10) AI analyst vs Human in the loop (19:20) Parsing pitch decks and new AI tools (23:10) How AI search is changing marketing (28:30) Powerful new capabilities and workflows (33:47) How quickly is everything changing?

EPISODE 150: How AI Is Transforming Diligence, Decision-Making & the Future of Investing Joe Lonsdale sits down with John Melas-Kyriazi, co-founder & CEO of Standard Metrics (00:00) Episode intro (02:10) Academia to investor and founder (08:00) The pain point that led to Standard Metrics (12:10) How AI is transforming diligence, data collection & VC (14:20) AI as a technical copilot (17:10) AI analyst vs Human in the loop (19:20) Parsing pitch decks and new AI tools (23:10) How AI search is changing marketing (28:30) Powerful new capabilities and workflows (33:47) How quickly is everything changing?

American Optimist

283,369 просмотров • 4 месяцев назад

A new episode of Hivemind is out now! This week we discuss the market rebound, DeFi exploit fallout, Crypto AI resurgence, and more. Timestamps: 0:00 Intro + market sentiment shift 08:55 The Aave/KelpDAO exploit explained 17:00 Why DeFi lending models may need to change 37:30 Where DeFi yield still makes sense 38:00 USDAI and real-world AI infrastructure yield 48:20 Venice, Bittensor, and AI token narratives 54:00 How AI agents are changing productivity 1:00:50 What the team is watching next

A new episode of Hivemind is out now! This week we discuss the market rebound, DeFi exploit fallout, Crypto AI resurgence, and more. Timestamps: 0:00 Intro + market sentiment shift 08:55 The Aave/KelpDAO exploit explained 17:00 Why DeFi lending models may need to change 37:30 Where DeFi yield still makes sense 38:00 USDAI and real-world AI infrastructure yield 48:20 Venice, Bittensor, and AI token narratives 54:00 How AI agents are changing productivity 1:00:50 What the team is watching next

Delphi Digital

12,596 просмотров • 4 месяцев назад

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

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 просмотров • 10 месяцев назад