Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Had a chat with Max Prasertsan around the Problem with AI Accountability! 3 topics we touched upon : ~ Why is "verifiable AI" different from AI audits or policies? ~ What happens when AI goes wrong? Who is accountable today? ~ How does cryptographic proof shift AI governance from... show more

Muskan Jain

20,787 subscribers

19,688 views • 11 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

As AI moves into robots, aviation, and surgery, one question matters more than performance: Who’s accountable when something goes wrong? This interview with Inference Labs 's co- founder Colin G (intern arc) dives into verifiable inference, cryptographic guarantees, and why trust is the missing layer for physical AI. TIMESTAMPS: 00:00 — Intro to Inference Labs and its mission 03:29 — Core AI verification and audit systems 05:51 — Why Web3 is needed for AI trust 11:28 — Robotics, safety, and accountability 18:52 — Human–robot trust and the future

As AI moves into robots, aviation, and surgery, one question matters more than performance: Who’s accountable when something goes wrong? This interview with Inference Labs 's co- founder Colin G (intern arc) dives into verifiable inference, cryptographic guarantees, and why trust is the missing layer for physical AI. TIMESTAMPS: 00:00 — Intro to Inference Labs and its mission 03:29 — Core AI verification and audit systems 05:51 — Why Web3 is needed for AI trust 11:28 — Robotics, safety, and accountability 18:52 — Human–robot trust and the future

Web3 Wesley

28,697 views • 6 months ago

AI agents are transforming how we work, but with innovation comes frauds. Trust is essential in the Agentic Economy🔑cheqd is building the AI Trust Layer powered by $CHEQ 🚀 ✅Watch how Verifiable AI ensures agents prove who they are to secure data & transactions

AI agents are transforming how we work, but with innovation comes frauds. Trust is essential in the Agentic Economy🔑cheqd is building the AI Trust Layer powered by $CHEQ 🚀 ✅Watch how Verifiable AI ensures agents prove who they are to secure data & transactions

cheqd.io 🆔

24,509 views • 1 year ago

“What we launched is the ability to assign a unique identifier to an AI agent, and relate that to a human” - evin, on the Deep Trust framework. Billions is creating a trust framework that makes AI: ✅ Unique ✅ Verifiable ✅ Assignable It’s how AI begins to work with us, not just for us.

“What we launched is the ability to assign a unique identifier to an AI agent, and relate that to a human” - evin, on the Deep Trust framework. Billions is creating a trust framework that makes AI: ✅ Unique ✅ Verifiable ✅ Assignable It’s how AI begins to work with us, not just for us.

Billions Network

21,178 views • 1 year ago

What does Verifiable AI mean? Turning “trust me” outputs into cryptographic proof, so agents can manage money, sign agreements and act with guarantees. 🎥 nader dabit breaks it down in less than 3 mins and shows how to start building ⬇️

What does Verifiable AI mean? Turning “trust me” outputs into cryptographic proof, so agents can manage money, sign agreements and act with guarantees. 🎥 nader dabit breaks it down in less than 3 mins and shows how to start building ⬇️

EigenCloud

13,119 views • 8 months ago

AI is everywhere, but the most impactful features start with a real user need. Ask how AI can add unique value to that experience, not just how to release an AI feature. Here are three questions to consider before you start building. 1️⃣ What’s the core user problem? 2️⃣ Does AI offer a unique solution? 3️⃣ What does AI take away from the user? Learn more:

AI is everywhere, but the most impactful features start with a real user need. Ask how AI can add unique value to that experience, not just how to release an AI feature. Here are three questions to consider before you start building. 1️⃣ What’s the core user problem? 2️⃣ Does AI offer a unique solution? 3️⃣ What does AI take away from the user? Learn more:

Google for Developers

10,415 views • 8 months ago

Why is Pi focusing on AI? What does AI have to do with blockchain? Pi Founder Chengdiao Fan explores how Pi is working to address the fundamental changes emerging in the AI era.

Why is Pi focusing on AI? What does AI have to do with blockchain? Pi Founder Chengdiao Fan explores how Pi is working to address the fundamental changes emerging in the AI era.

Pi Network

229,397 views • 4 months ago

"AI doesn't take your job. AI makes you the CEO." Balaji Srinivasan joins a16z’s Erik Torenberg for a conversation on the future of the AI economy, decentralization, and how work changes in an AI-native world, including: - How distillation and open source could decentralize AI power - Why AI lowers the cost of creation but raises the cost of verification - The shift from global internet to “trusted tribes” and private AI - Why humans are the sensor and AI is the actuator 00:00 Intro 02:06 Why you want AI inside the trusted tribe, not outside it 05:35 The Problem with AI slop 09:25 Where AI works 17:08 "AI can't read your mind, but it can read your body." 30:10 "AI doesn't take your job. AI makes you the CEO." 46:01 The SaaSpocalypse: Real or overblown? 49:19 What happens if AI companies get bigger than governments? Balaji Erik Torenberg

"AI doesn't take your job. AI makes you the CEO." Balaji Srinivasan joins a16z’s Erik Torenberg for a conversation on the future of the AI economy, decentralization, and how work changes in an AI-native world, including: - How distillation and open source could decentralize AI power - Why AI lowers the cost of creation but raises the cost of verification - The shift from global internet to “trusted tribes” and private AI - Why humans are the sensor and AI is the actuator 00:00 Intro 02:06 Why you want AI inside the trusted tribe, not outside it 05:35 The Problem with AI slop 09:25 Where AI works 17:08 "AI can't read your mind, but it can read your body." 30:10 "AI doesn't take your job. AI makes you the CEO." 46:01 The SaaSpocalypse: Real or overblown? 49:19 What happens if AI companies get bigger than governments? Balaji Erik Torenberg

a16z

162,795 views • 3 months ago

Different AI tools and what they actually do. Meanwhile, guess who shot this video today? My AI clone. If you want to learn how to create videos like this with AI. Comment “AI” to join the AI Income Hub

Different AI tools and what they actually do. Meanwhile, guess who shot this video today? My AI clone. If you want to learn how to create videos like this with AI. Comment “AI” to join the AI Income Hub

Kemi

13,843 views • 1 month ago

General purpose AI got us here but it won’t take us where we’re going. When everyone is using the same models, there’s no edge. The shift is clear from renting AI to owning AI and that's what Manos Koukoumidis is pushing forward with Oumi. This is where AI gets real.

General purpose AI got us here but it won’t take us where we’re going. When everyone is using the same models, there’s no edge. The shift is clear from renting AI to owning AI and that's what Manos Koukoumidis is pushing forward with Oumi. This is where AI gets real.

Farlane

50,319 views • 3 months ago

Sam Altman explains the fire code model for AI safety: stop pretending labs can block every dangerous use, build resilience before bio or cyber goes visibly wrong. "The shift that I think the world needs to make for AI security generally, and biosecurity in particular, is to move from one of blocking to one of resilience." "Fire did all these wonderful things for society. Then it started burning down cities. We tried to do all of these things to restrict fire." "Curfew comes from when you were not allowed to have fires anymore because they were burning down cities. And then we got better at resilience to fire. We came up with fire code and flame resistant materials." "I think we need to think about AI the same way. AI is going to be a real problem for bioterrorism. AI is going to be a real problem for cybersecurity. AI is also a solution to those things." "We need a society-wide effort to provide the infrastructure for this resilience, not labs that we trust to always block what they are supposed to block." "If something goes really wrong, visibly really wrong for AI this year, I think bio would be a reasonable bet for what that could be. And then as we get into next year and the following year, you can imagine lots of other things going really wrong too." Most AI safety debates still treat the model lab as the main checkpoint. Sam is describing a different stack: fire code for AI, resilience infrastructure, bio researchers, cyber defense, many good models, and public systems that keep working when blocking fails. Call it the assurance layer: who tests, who audits, who verifies, who discloses, who is accountable when AI enters biological, cyber, medical, financial, and government workflows. P.S. This is exactly why we are convening the AI Assurance & Governance Summit at Stanford Faculty Club on Oct 1, 2026: frontier labs, regulated industries, VCs, researchers, insurers, and practical trust evaluation in one room. Reserve a seat: Source: Sam Altman (Sam Altman), CEO of OpenAI, at OpenAI Town Hall.

Sam Altman explains the fire code model for AI safety: stop pretending labs can block every dangerous use, build resilience before bio or cyber goes visibly wrong. "The shift that I think the world needs to make for AI security generally, and biosecurity in particular, is to move from one of blocking to one of resilience." "Fire did all these wonderful things for society. Then it started burning down cities. We tried to do all of these things to restrict fire." "Curfew comes from when you were not allowed to have fires anymore because they were burning down cities. And then we got better at resilience to fire. We came up with fire code and flame resistant materials." "I think we need to think about AI the same way. AI is going to be a real problem for bioterrorism. AI is going to be a real problem for cybersecurity. AI is also a solution to those things." "We need a society-wide effort to provide the infrastructure for this resilience, not labs that we trust to always block what they are supposed to block." "If something goes really wrong, visibly really wrong for AI this year, I think bio would be a reasonable bet for what that could be. And then as we get into next year and the following year, you can imagine lots of other things going really wrong too." Most AI safety debates still treat the model lab as the main checkpoint. Sam is describing a different stack: fire code for AI, resilience infrastructure, bio researchers, cyber defense, many good models, and public systems that keep working when blocking fails. Call it the assurance layer: who tests, who audits, who verifies, who discloses, who is accountable when AI enters biological, cyber, medical, financial, and government workflows. P.S. This is exactly why we are convening the AI Assurance & Governance Summit at Stanford Faculty Club on Oct 1, 2026: frontier labs, regulated industries, VCs, researchers, insurers, and practical trust evaluation in one room. Reserve a seat: Source: Sam Altman (Sam Altman), CEO of OpenAI, at OpenAI Town Hall.

Karl Mehta

13,973 views • 8 days ago

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

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

EigenCloud

62,099 views • 9 months ago

What happens when AI is given a body? @FryRSquared visits UC Berkeley, where researchers find out how AI physically interacts with the world. More on The Future with Hannah Fry

What happens when AI is given a body? @FryRSquared visits UC Berkeley, where researchers find out how AI physically interacts with the world. More on The Future with Hannah Fry

Bloomberg Technology

22,731 views • 1 year ago

The future is being built with AI, fast. But can we prove it’s working correctly? This is why verifiable AI matters. Watch what happened last week and why it matters for Polyhedra's mission. 👇

The future is being built with AI, fast. But can we prove it’s working correctly? This is why verifiable AI matters. Watch what happened last week and why it matters for Polyhedra's mission. 👇

Polyhedra

25,471 views • 11 months ago

New conversation with Henry Shevlin and Robert Long on AI consciousness and welfare! Among many other topics, we discuss: - Why we should take AI consciousness and welfare seriously - What Rob found doing the first external welfare evaluation of a frontier model, Claude, and his experiments on Claude Mythos - The "willing servitude" problem: if AI loves being helpful, is that good or horrifying? - Why AI companies might have an incentive to downplay AI consciousness (1/2)

New conversation with Henry Shevlin and Robert Long on AI consciousness and welfare! Among many other topics, we discuss: - Why we should take AI consciousness and welfare seriously - What Rob found doing the first external welfare evaluation of a frontier model, Claude, and his experiments on Claude Mythos - The "willing servitude" problem: if AI loves being helpful, is that good or horrifying? - Why AI companies might have an incentive to downplay AI consciousness (1/2)

Dan Williams

36,645 views • 2 months ago

From: Toucher & Hardy: Here's What Happens When AI is Prompted to Run a Radio Station

From: Toucher & Hardy: Here's What Happens When AI is Prompted to Run a Radio Station

98.5 The Sports Hub

11,473 views • 1 month ago

AI is getting faster. Misinformation is getting louder. In this conversation, Dennis Yap talks with Luca Allam about why the future of AI depends on verification, not vibes. At AI Seer, we’re building Facticity to authenticate claims, disambiguate context, and surface real sources, no speculation, no spin. The future of AI isn’t what it can generate. It’s what we can trust. Listen to the full podcast :

AI is getting faster. Misinformation is getting louder. In this conversation, Dennis Yap talks with Luca Allam about why the future of AI depends on verification, not vibes. At AI Seer, we’re building Facticity to authenticate claims, disambiguate context, and surface real sources, no speculation, no spin. The future of AI isn’t what it can generate. It’s what we can trust. Listen to the full podcast :

AI Seer

237,705 views • 6 months ago

Every week, AI moves deeper into our bodies, our jobs, our economies—even our culture. With that speed comes risk: 🍳 How do we verify what’s happening under the hood? ⚖️How do we align these systems with human values before they run ahead of us? This is why verifiable AI matters. Not just innovation—trust, safety, accountability.

Every week, AI moves deeper into our bodies, our jobs, our economies—even our culture. With that speed comes risk: 🍳 How do we verify what’s happening under the hood? ⚖️How do we align these systems with human values before they run ahead of us? This is why verifiable AI matters. Not just innovation—trust, safety, accountability.

Polyhedra

20,512 views • 10 months ago

This is what happens when open-source AI merges with traditional filmmaking! Here’s my favorite sequence from our new AI movie Unique Perspective! FULL PROCESS below👇

This is what happens when open-source AI merges with traditional filmmaking! Here’s my favorite sequence from our new AI movie Unique Perspective! FULL PROCESS below👇

Mickmumpitz

12,988 views • 10 months ago

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Dwarkesh Patel

285,710 views • 13 days ago