The podcast discusses the surprising departure of Sam Altman from OpenAI, the impact of AI in different workflows, and three categories of AI development. They also explore Lindy, a startup automating tasks with AI, and discuss the product Dot, an AI guide. The podcast highlights the importance of connecting generative and deterministic systems, automating tasks using platforms, and the future of work co-pilots.
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Quick takeaways
There are three buckets of innovation in AI product development: efficiency, teleportation, and inventing new interfaces and behaviors.
Lindy is an AI startup that aims to automate tasks and orchestrate different systems through a user-friendly interface, showing potential in improving efficiency and productivity.
Deep dives
The Three Buckets of Innovation
The author, Rory Sutherland, presents three buckets of innovation. The first bucket focuses on making faster horses and is primarily about efficiency. The second bucket is about teleportation, which is a sci-fi concept that everyone agrees would be awesome. The third bucket is referred to as the 'Japanese toilet problem,' where you discover something you never knew you needed until you experience it. Each bucket requires different founders at different times. The first bucket involves executing the roadmap to improve efficiency, while the second and third buckets require patience and craft to invent new interfaces and behaviors.
Exploring Lindy
Lindy is a startup that aims to build AI employees, allowing users to automate tasks by stitching together different workflows using AI. It can automate tasks like scheduling meetings by copying into an email, suggesting available times, sending calendar invites, and responding to the original thread. Lindy combines natural language understanding with generative capabilities to reason through errors and find solutions. Although it is still a work in progress, Lindy shows potential in automating tasks and orchestrating different systems through a user-friendly interface.
GPT's Role in Prompting and API Interaction
GPT models play a role in prompting and connecting with APIs, bridging the gap between generative interfaces and fixed-syntax systems. Prompting GPT models requires a mix of natural language and syntactical input. The goal is to make the interaction feel natural and enable users to speak in languages they understand, while the models handle the communication with specific APIs. The future potential lies in empowering GPT models to directly interact with APIs to gather data and provide more connected and useful experiences for users.
The Creation of New Interfaces and Behaviors
The advent of AI opens up opportunities for creating new interfaces and behaviors. Dot, a product built by New Computer, serves as a guide for users' lives. It captures and remembers conversations, applying memory and empathy to provide relevant suggestions or reminders. Dot aims to navigate the boundary between utilitarian and empathetic interactions, offering a unique user experience. This category of AI products bridges the gap between work-based co-pilots and personal life guides, focusing on understanding and assisting users in navigating their lives with a mix of utility and humanity.
The conversation kicks off with the surprising news of Sam Altman’s departure from OpenAI. Nabeel and Fraser then discuss the ‘crazy week’ of product releases, demystifying the complexity and impact of AI in various workflows. They spotlight AI startup Lindy, review My GPTS, and early impressions of the product Dot, an AI-driven guide in daily life. The duo categorizes AI developments into three ‘buckets’: lead bullet strategy, a universal translator bucket, and inventing new behaviors.
(00:00) - The three types of LLM products
(02:07) - Exploring Lindy
(08:21) - GPTs
(15:52) - Frameworks for AI Product Development
(20:21) - Dot: Your Guide in Life
(26:07) - Revisiting Categorization
(29:45) - Patience in Innovation
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