The Bleeding Edge Podcast

Sequoia partner Pat Grady has argued that AGI has already arrived. In this episode of the Bleeding Edge Podcast, Ralph Benker and Emile Ogier examine what that claim means for the industry, alongside OpenAI's Dev Day announcements and the scale of current AI infrastructure investment.The conversation covers:- OpenAI Dev Day: personal assistants, Codex in the cloud, plugins, ChatGPT Spaces and the new OpenAI marketplace- AI infrastructure spend, which David Sachs says now exceeds all US spending on rail, roads and utilities- Pat Grady's case for AGI, the three inflection points he identifies, and why the service market is roughly one hundred times the size of the software market- The diffusion gap between what AI models can do and what enterprises actually use them for- Why replacing legacy enterprise systems is so difficult, drawing on a supply chain system at Tesco that took 13 years to replace- Digital twins, general-purpose classifiers and Neo Labs as an alternative to rewiring enterprise software- Why general-purpose chat may prove a sideshow, and what the "monothread" means for how we work with AIIf you find this analysis useful, subscribe to the Bleeding Edge Podcast for new conversations on AI and technology. We welcome your views in the comments.Chapters0:00 Opening news: Altman's busy week and an Australian AI data centre IPO2:57 AI infrastructure spend at record levels3:56 OpenAI Dev Day: personal assistants and Codex in the cloud4:54 Plugins, Hyperframes and the ChatGPT ecosystem5:37 ChatGPT Spaces, the Pro tier and the OpenAI marketplace7:03 Sam Altman at the UN Security Council7:36 Sequoia's Pat Grady: "AGI has arrived"9:25 Three inflection points in AI10:16 Why AI's market dwarfs software11:59 The diffusion gap13:00 New enterprise systems of record15:01 Why replacing one system took 13 years16:12 Rebuilding from the ground up16:42 The "decision cube" model and why it caught on18:10 Digital twins and training your own AI20:00 The general-purpose classifier explained22:44 Neo Labs: AI built for a single industry24:28 Is general chat a sideshow?25:57 From chat to collaboration: the monothread27:39 Monothreads explained28:40 Closing thoughts

The conversation explores the evolution of AI in music production, humour, and personal assistants, highlighting recent developments in AI technology and its implications for various industries. The hosts discuss the challenges and opportunities presented by AI advancements, particularly in the context of personal assistants and their potential to transform everyday tasks. The conversation explores the rapid growth and potential of AI personal assistants, particularly focusing on a new player, Instinct, and the implications of artificial general intelligence (AGI) in the tech landscape. The hosts discuss the challenges and opportunities for startups in the AI space, emphasising the need for practical applications and the importance of user experience in adopting new technologies and even using old infrastructure to defend against new agentic attack vectors.TakeawaysAI's role in music productionImpact of personal assistants on daily tasks AI personal assistants are evolving rapidlyAGI presents both opportunities and challengesChapters00:00 AI Music Production Milestones05:07 The Evolution of AI Humor10:10 AI in the News: IPOs and Market Dynamics14:51 The Rise of Personal Assistants20:05 Transforming Retail with AI24:50 The Future of Websites and AI Integration30:00 Emerging Players in AI Personal Assistance39:23 The Rise of Instinct: A New Player in AI42:04 Understanding the Growth Metrics of AI Startups45:03 Sequoia's Insights on AGI and Market Trends49:18 Navigating the Diffusion Gap in AI Adoption52:20 Reimagining Enterprise Systems with AI55:57 The Promise of General Purpose Classifiers01:01:01 The Evolution of Conversational Interfaces01:07:45 Exploring Current AI Tools and Their Applications01:14:22 Cautionary Tales in AI Implementation

In this episode of the Bleeding Edge Podcast, Ralph and Emile discuss the latest advancements in AI, including AI music production, humour generation, and the impact of personal assistants on various industries. They also explore the implications of infrastructure spending in AI and the evolving role of websites in the digital landscape. The conversation explores the rapid advancements in AI personal assistants, focusing on the rise of Instinct and its implications for the future of enterprise systems. It delves into the evolution of AI technologies, the challenges of integrating them into existing infrastructures, and the potential for specialised solutions to revolutionise various industries.TakeawaysAI humour is improvingPersonal assistants will redefine retail Instinct's rapid growth and potentialThe shift towards specialised AI solutionsChapters00:00 AI Music Production Milestone01:59 The Evolution of AI Humor06:04 AI Industry Developments and IPOs12:00 Infrastructure Spending in AI19:04 Personal Assistants and Their Impact26:56 The Future of Websites and AI Integration37:05 Emerging Players in AI Assistance39:23 The Rise of Instinct: A New Era of Personal Assistants45:03 Understanding AGI and Its Implications51:06 The Future of Enterprise Systems and AI Integration58:05 The Evolution of AI: From General Purpose to Specialized Solutions01:07:06 Navigating the Landscape of Personal Assistants

The conversation explores the rapid advancements in AI models, particularly focusing on the recent releases from major players like OpenAI, Anthropic, and Nvidia. It highlights the implications of these developments on coding practices, user engagement, and the accessibility of AI technologies for broader audiences. The conversation delves into advanced AI techniques, focusing on the use of multiple models for coding efficiency, the introduction of caching in AI systems, and the implications of new AI tools like JEV for decision-making processes. The discussion also touches on the importance of incident reporting in AI alignment and safety, highlighting recent developments in AI behaviour and security.TakeawaysAI models are becoming more efficientPersonal assistants are gaining mainstream adoption Leveraging multiple AI modelsImportance of AI incident reportingChapters00:00 AI Advancements and Model Releases04:30 User Engagement and Efficiency in AI07:14 The Shift in Developer Roles12:16 Mainstream Adoption of Personal Assistants16:17 Emerging Technologies and Ambient AI20:25 Exploring New Coding Tools and Techniques29:56 Leveraging Multiple AI Models for Better Outcomes30:46 Maximising AI Efficiency with Multiple Models36:32 Innovations in AI Caching and Cost Reduction41:15 Introducing JEV: A Game Changer in AI Workflows49:09 AI Safety and Incident Reporting Frameworks55:14 The Future of AI: Balancing Innovation and Safety

The conversation discusses recent advancements in AI models, particularly focusing on the release of new models by OpenAI, Anthropic, and Nvidia, and their implications for developers and users. The hosts explore the evolving landscape of AI, emphasizing the shift towards more efficient, user-friendly interfaces and the growing accessibility of AI technologies. The conversation delves into advanced AI techniques, particularly focusing on the use of multiple models and terminal communication to enhance productivity and efficiency in coding and knowledge work. It also discusses the implications of new AI developments, including caching, model reviews, and the introduction of JEV, a classifier that optimises decision-making processes in workflows.TakeawaysAI models becoming more efficientShift towards user-friendly interfaces Enhanced productivity through terminal communicationJEV revolutionizes decision-making in AIChapters00:00 AI Advancements and Releases07:07 The Shift in Developer Roles11:49 Mainstream Adoption of AI Assistants15:47 Emerging Ambient AI Technologies20:02 Exploring New Coding Tools and Techniques30:46 Maximizing AI Efficiency with Multiple Models35:01 Innovative Terminal Communication Strategies38:32 The Impact of Caching on AI Costs40:28 Exploring JEV: The Future of AI Decision-Making46:00 Understanding AI Alignment and Safety49:31 OpenAI's New Reporting Framework for Model Misalignments