For decades (if not centuries if we take non-digital systems), we’ve been using and needing data for everything, everywhere, and it’s now become mission-critical not only for almost any company, but for society as a whole. It’s also been feeding algorithms and models to create the recent wave of generative AI that we are all discovering and experiencing.

However, there’s a shift happening in how we use data : areas of once useful data are now hijacked by generative AI. Some data are simply becoming irrelevant ; there’s just no need anymore to access and use them, as generative AIs do the job (for better or worse). These large language models still depend on them, but to what extent going forward ? What kind of data will remain critical ?

This talk aims to present the evolving relationship between data and generative AI, through 1) examples and case studies from recent projects and implementations, 2) conceptualizing this paradigm shift, 3) heat-mapping data obsolescence and 4) providing insights into how to deal with the current and future implications, including the challenges ahead in ethics and privacy.

Key takeaways for the attendees :
- Understand the shift from our existing dependency on data to our coming dependency on generative AI
- Understand the implications, opportunities and risks for companies and startups relying on data and/or AI
- Provide advice on how to address these challenges

This talk will be of interest to anyone working in an environment that heavily relies on data, and companies that build and monetize products and services that depend on data. They will have a better understanding of the impact of generative AI on their data needs, and how to shift their operating and/or business models accordingly.

I designed the talk to be around 50 min + 10 min Q&A but this can be adapted depending on the need to go in-depth technically, or to show demos. Also, the talk is agnostic and does not recommend specific providers or companies.