Ryan Salva, Senior Director of Product at Google and former lead for GitHub Copilot, shares his journey in creating this groundbreaking AI coding tool. He discusses the fine line between novice and expert developer expectations from AI, revealing the double-edged sword of predictive text. Salva also reflects on the rapid evolution of AI tools, the future of developer roles amidst rising automation, and the necessity of effective evaluation methods in AI development. His insights point to a transformative future for software engineering.

James Theuerkauf, CEO of Syrup Tech, and Sara Ittelson, Partner at Accel, share their insights on the challenges of building AI-first companies. They dive into innovative demand forecasting in retail, enhancing decision-making with AI. The duo discusses leveraging unconventional data to boost business performance and the evolving role of generative AI in shaping industries. They emphasize the importance of adaptability and a problem-centric mindset for AI entrepreneurs, ultimately arguing that AI's transformative potential is often underappreciated.

Noam Rubin, a Software Developer at Vanta, specializes in creating AI tools for security and compliance. He shares fascinating insights into using data-driven strategies for building AI features. Discover how customer feedback and rapid prototyping are vital in transforming ideas into practical tools. Noam explains the importance of integrating AI into compliance workflows, maintaining product quality through iterative testing, and educating engineers to democratize AI. His experiences highlight the balance between innovation, user empathy, and technical accuracy.

In this episode of High Agency, former OpenAI researcher Stan Polu shares his journey from AI research to founding Dust, an enterprise AI platform. Stan offers a contrarian view on the future of AI, suggesting we may be hitting a plateau in model capabilities since GPT-4. He discusses why startups should focus on product-market fit before investing in GPUs, shares practical lessons for building AI products, and predicts increased competition between AI labs and API developers. Chapters:00:00 - Introducing Dust: an enterprise AI platform06:07 - From Stripe to OpenAI: Stan's journey10:29 - Why research wasn't enough: building Dust15:10 -  Best practices for building an AI product20:50 - Is prompt engineering here to stay23:40 - Understanding language models and their limitations32:56 - Predictions for AI in 202539:53 - Measuring progress toward AGI42:26 - The true value of AI technology--------------------------------------------------------------------------------------------------------------------------------------------------Humanloop is the LLM evals platform for enterprises. We give you the tools that top teams use to ship and scale AI with confidence. To find out more go to humanloop.com

Join Ben Fershman, CEO and co-founder of Replicate AI, as he discusses how their platform is revolutionizing AI development by making it accessible through open-source resources. He dives into the importance of democratizing AI for all users, from hobbyists to professionals. Ben explores the transformative advancements in AI image generation, including innovative techniques like 'control net.' He also addresses the fragmentation of AI models and emphasizes the need for customization and hands-on experimentation in navigating the evolving landscape of AI technology.

Lorilyn McCue, Product Manager at Superhuman, discusses her journey from Apache helicopter pilot to leading AI features in email. She shares the importance of a 'learning-first' approach and leveraging user feedback to create practical tools like Ask AI and instant replies. The conversation explores balancing on-demand versus always-on AI features, prompt engineering for effective summarization, and the seamless integration of AI into user workflows. Lorilyn emphasizes the need for continuous improvement and staying updated in the fast-evolving AI landscape.

In this discussion, Jeff Huber, founder of Chroma, shares insights on vector databases and their critical role in AI engineering. He dives into the issues surrounding retrieval-augmented generation (RAG) terminology, advocating for clearer language in the field. Jeff details the evolution of Chroma, focusing on developer experiences and real-world applications, while also debating the timelines for achieving super AI. Listeners will learn about embedding processes, the significance of context in AI, and the challenges of AI deployment in production systems.

Peter Gostev, Head of AI at Moonpig and former AI strategy lead at NatWest, shares valuable insights on creating effective AI strategies in large enterprises. He discusses the challenges of deploying LLMs, particularly in traditional banking and e-commerce. Peter highlights the importance of supportive leadership and the need for hands-on experience in AI implementation. The conversation also touches on underappreciated developments in AI, the automation of customer service, and how innovative projects can emerge from everyday workflows.

Surojit Chatterjee, CEO and founder of Ema, a company revolutionizing AI-powered workflow automation, shares his journey from tech titan to entrepreneur. He discusses the challenges of building a universal AI employee and highlights innovative use cases in Fortune 1000 companies. Surojit also explores evolving SaaS business models, the complexities of AI integration, and the balance between AI and human roles, emphasizing a collaborative future where AI enhances human creativity and decision-making.

Hamel Husain, a veteran AI consultant and engineer with a rich background at GitHub and Airbnb, teams up with Swyx, the founder of the AI Engineer World Fair. Together, they delve into the vital role of evaluations in AI product development and discuss common pitfalls that developers face. They explore the concept of literate programming, highlighting its potential to enhance software quality. The conversation reveals the importance of understanding AI prompts and effective metrics to navigate the complexities of AI innovation.