The Data Storytellers Podcast
Why Most AI Use Cases Miss the Point | Taylor Black (Microsoft)
What happens when a technology starts to look, sound, and reason like us? Taylor Black, Director of the AI Ventures Ecosystem in the CTO’s Office at Microsoft, has spent his career at the intersection of data, venture building, and emerging technology. From bootstrapping a SaaS company to helping shape early stage AI innovation inside one of the world’s largest technology organizations, Taylor brings a rare long term perspective on what AI is, and what it is not. In this conversation, we explore how today’s AI moment compares to past technological revolutions, why most current AI applications are still low hanging fruit, and where the real economic and organizational disruption is likely to come from next. We discuss the limits of automation, the rise of agentic systems, the moral responsibility of those building AI, and why the hardest work ahead is not technical, but cultural. This is a wide ranging discussion on AI, human work, governance, and the difference between making hard things easier versus making impossible things merely difficult. Connect with us: Website: https://thedatastorytellers.com/ LinkedIn: https://www.linkedin.com/company/the-data-storytellers Apple Podcasts: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476 Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA Chapters 00:00 – Introducing Taylor Black and his role at Microsoft 04:00 – From bootstrapped SaaS founder to data driven decision making 10:30 – Learning to translate technical insight into business value 16:30 – Venture studios, zero to one work, and entering Microsoft 23:30 – How ChatGPT changed perception inside large organizations 28:00 – The steam engine analogy and why infrastructure matters 33:30 – AI, work, and why modern jobs already felt broken 40:00 – Presence, remote work, and the limits of digital collaboration 46:00 – AI optimism, AI fear, and misunderstanding the technology 52:00 – What large language models actually are and are not 58:30 – Governance, moral responsibility, and who shapes AI 1:05:30 – Agentic systems, job disruption, and what comes next
