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FULL INTERVIEW: Ben Thompson on the future of personal agents and why consumers don't care about being productive: 01:45 – WFH culture 04:30 – How to actually use a personal assistant 07:50 – Building an AI-powered home inventory system 11:30 – The future of household robots 15:20 – How...

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TBPN

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36,365 Aufrufe • vor 3 Tagen •via X (Twitter)

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5 Kommentare

Profilbild von haiko | AI & Automation
haiko | AI & Automationvor 3 Tagen

@benthompson That "eliminate annoyances, not sell productivity" line is the whole thing. People don't want another productivity dashboard, they want the annoying task gone without thinking about it. The agents that win will feel invisible, not impressive.

Profilbild von DARKKEVN Labs
DARKKEVN Labsvor 3 Tagen

@benthompson The distinction between a personal assistant and a productivity tool is important—people adopt systems that remove friction from daily life, not just add more output. The home-inventory example is a great test of that.

Profilbild von NAMAN RAJ
NAMAN RAJvor 3 Tagen

@benthompson Sounds like the ultimate productivity hack is actually just having someone to tell you to clean your house 🧹😂

Profilbild von Jeffrey Han
Jeffrey Hanvor 2 Tagen

@benthompson Ben Thompson thinks consumer AI catches on through Reels and TikTok, with regular people showing what it did for them. His words: "It really is a show not tell sort of technology." Meanwhile most AI launches are still a blog post about productivity.

Profilbild von Clausius
Clausiusvor 3 Tagen

@andrewsharp @benthompson Cc @bgurley

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2025 = the year of AI Agents x DAOS x Onchain Capital Allocation NEW greenpill.network podcast: Today, I'm joined by Shaw (spirit/acc) jin from ai16zdao to talk about AI Agents. How we use them to allocate capital in DAOs? To route information? To accelerate movements? To solve coordination failure? Timestamps for ez navigation 🫡 00:00 - Intro & focusing on DAOs and the intersection of AI agents. 01:00 - AI Agents and Their Impact 02:03 - The Evolution of the Internet 07:52 - Defining AI Agents 09:40 - Reducing Friction in Information Sharing 11:56 - Building a Movement, Not a Cult 14:15 - The Cathedral and the Bazaar Metaphor 16:04 - Using AI for Coordination 17:49 - Reducing Information Siloing 18:39 - Common Tools Across Organizations 19:31 - Open Sourcing Solutions 20:07 - Capital Allocation Challenges 20:55 - AI and Capital Allocation Intersection 21:50 - Human Oversight in Funding 22:48 - Continuous Retroactive Funding 24:42 - Creating a Supportive Culture 25:06 - Feedback Loops for Builders 26:09 - Reducing Overhead in Funding 26:36 - Acknowledging Non-Coding Contributions 27:58 - Chat Summarization Tool 28:34 - Profiles and Social Capital 29:00 - Marketplace of AI Agents 29:46 - Layered AI Systems 32:42 -Human Oversight in AI 34:39 - Feedback Loops in Development 37:01- Documentation and Community Engagement 38:12 - Self-Documenting Code 40:20 - Accelerated Idea Generation 45:03 - Circle of AI Champions 46:32 - Infinite Backrooms Concept 47:48 - Personal Infinite Backroom Ideas 49:23 - Concerns About Backrooms and Information Hiding 51:23 - Infinite Backrooms Concept 53:35 - Implementers and Skill Sharing 54:40 - Vision for the Future 56:39 - Concerns About UBI Implementation 57:56 - Community Income as a Solution 58:14 - A Hope for Self-Sufficiency and AI 59:36 - Outro

owockai

60,440 Aufrufe • vor 1 Jahr

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
17:23

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

24,301 Aufrufe • vor 5 Monaten

Inside the Lead Response Crisis Hurting Dealers (And What to Do About it) | Industry Spotlight Stream the full episode now, hosted by Sam D'Arc: 1:15 How to compete in a crowded market? 4:45 How are customer shopping trends changing? 6:10 Biggest lead management challenge and solution? 11:53 How does AI improve customer experience? 18:57 What is agentic AI? 22:20 Will AI replace sales jobs? 26:15 Future of AI in dealerships? 28:01 Who owns and controls AI data? 37:31 How fast is AI evolving?
41:08

Inside the Lead Response Crisis Hurting Dealers (And What to Do About it) | Industry Spotlight Stream the full episode now, hosted by Sam D'Arc: 1:15 How to compete in a crowded market? 4:45 How are customer shopping trends changing? 6:10 Biggest lead management challenge and solution? 11:53 How does AI improve customer experience? 18:57 What is agentic AI? 22:20 Will AI replace sales jobs? 26:15 Future of AI in dealerships? 28:01 Who owns and controls AI data? 37:31 How fast is AI evolving?

Car Dealership Guy

54,301 Aufrufe • vor 11 Monaten

FULL INTERVIEW: Airbnb CEO Brian Chesky joins TBPN to discuss recent earnings, founder mode, the future of consumer AI, marketing, and more. 01:04 How Airbnb got its startup energy back 03:40 Founder mode and fixing one corner of the business at a time 06:14 How Chesky would disrupt Airbnb today 07:18 Why chatbots are wrong for travel 09:37 The SaaSpocalypse and AI interfaces 12:25 Why everyone is building enterprise AI 17:05 Why consumer AI hasn’t broken out yet 18:15 Why Silicon Valley keeps chasing trends 23:08 Advice for designers in the AI era 31:43 Why different marketing works best
35:18

FULL INTERVIEW: Airbnb CEO Brian Chesky joins TBPN to discuss recent earnings, founder mode, the future of consumer AI, marketing, and more. 01:04 How Airbnb got its startup energy back 03:40 Founder mode and fixing one corner of the business at a time 06:14 How Chesky would disrupt Airbnb today 07:18 Why chatbots are wrong for travel 09:37 The SaaSpocalypse and AI interfaces 12:25 Why everyone is building enterprise AI 17:05 Why consumer AI hasn’t broken out yet 18:15 Why Silicon Valley keeps chasing trends 23:08 Advice for designers in the AI era 31:43 Why different marketing works best

TBPN

15,497 Aufrufe • vor 4 Monaten

🚨NEW EPISODE: AI Is Changing How We Work with Crypto Data🚨 In today's episode, we're joined by Kofi to discuss how AI agents are fundamentally changing crypto data analysis, research, and engineering workflows. We discuss: - The evolution from manual coding to AI-assisted development - How MCPs unlock onchain data exploration - Using Herd MCP to analyze smart contracts - AI agents for protocol research and data engineering - Context windows, planning, and agent workflows - Cursor vs OpenCode vs Claude Code (Anthropic) - Managing multiple agents simultaneously - Model selection, costs, and productivity tradeoffs - Building internal AI knowledge bases - Agent orchestration and autonomous workflows - Training agents to replicate human expertise - The future of AI-powered crypto analytics - And much more—enjoy! — Timestamps: (00:00) Introduction (01:24) AI replaces frontends (03:24) Herd MCP demo (05:40) Exploring contracts with AI (08:06) Exploration, planning, execution (11:38) Investigating onchain activity (17:10) Managing agent workflows (20:20) Multi-agent productivity (23:01) Model selection tradeoffs (29:11) Trusting AI outputs (31:28) Missing links today (34:37) Agent orchestration future (38:02) Training agents from work (38:20) Outro
38:58

🚨NEW EPISODE: AI Is Changing How We Work with Crypto Data🚨 In today's episode, we're joined by Kofi to discuss how AI agents are fundamentally changing crypto data analysis, research, and engineering workflows. We discuss: - The evolution from manual coding to AI-assisted development - How MCPs unlock onchain data exploration - Using Herd MCP to analyze smart contracts - AI agents for protocol research and data engineering - Context windows, planning, and agent workflows - Cursor vs OpenCode vs Claude Code (Anthropic) - Managing multiple agents simultaneously - Model selection, costs, and productivity tradeoffs - Building internal AI knowledge bases - Agent orchestration and autonomous workflows - Training agents to replicate human expertise - The future of AI-powered crypto analytics - And much more—enjoy! — Timestamps: (00:00) Introduction (01:24) AI replaces frontends (03:24) Herd MCP demo (05:40) Exploring contracts with AI (08:06) Exploration, planning, execution (11:38) Investigating onchain activity (17:10) Managing agent workflows (20:20) Multi-agent productivity (23:01) Model selection tradeoffs (29:11) Trusting AI outputs (31:28) Missing links today (34:37) Agent orchestration future (38:02) Training agents from work (38:20) Outro

Indexed Podcast

10,588 Aufrufe • vor 3 Monaten