SummaryIn this conversation, the founders of Bauplan, Jacopo and Ciro, share their extensive backgrounds in AI and data infrastructure, discussing the evolution of NLP and the challenges faced in the industry. They highlight the importance of data pipelines in AI effectiveness and the complexities of building data infrastructure. The discussion also covers lessons learned from previous ventures, the shifting dynamics of the AI market, and the need for collaboration between data scientists and engineers. They emphasize the significance of simplicity in data tools and the future of data management focusing on standardization and accessibility.In this episodeBauplan was founded by experienced professionals in AI and data.Data challenges remain significant despite advancements in AI.Lessons from previous ventures inform current strategies.Building data infrastructure is complex and requires careful planning.Collaboration between data scientists and engineers is essential.Data engineering will resemble more and more software engineering.Simplicity in data tools can enhance user experience.The future of data management will focus on standardization and accessibility.If you care about making AI features shippable by regular software teams—not just data specialists—this conversation maps the terrain and the trade-offs.Chapters00:00 Introduction to Bauplan and Founders' Background02:27 The Evolution of NLP and AI Challenges05:05 Shifts in Data and AI Application07:56 Lessons from Previous Ventures10:20 The Search Market Landscape13:05 Behavioral Data's Role in Search15:52 Building Data Infrastructure vs. Applications18:22 The Complexity of Data Management21:03 Bridging the Gap Between Data Science and Engineering23:39 Challenges in Infrastructure Development29:52 Navigating the Infrastructure Landscape32:19 The Pendulum of Centralization and Decentralization34:00 The Need for Standardization in Data Infrastructure36:52 Simplifying Data Workflows40:29 Radical Simplicity in Data Management45:28 Overcoming Resistance to Change48:50 The Future of Data Abstractions and Git for Data

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