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The biggest AI breakthroughs won’t come from Ph.D. labs — they’ll come from people solving real-world problems. So how do AI founders actually turn cutting-edge research into real products and scale them? In this week’s episode of Founded & Funded, Madrona Partner Jon Turow sat down with Jonathan Frankle, Chief AI Scientist at Databricks to talk about the shift from AI hype to real adoption — and what founders need to know.
They dive into:
1) How AI adoption has shifted from hype to real-world production
2) The #1 mistake AI startups make when trying to sell to enterprises
3) Why your AI system shouldn’t care if it’s RAG, fine-tuned, or RLHF — it just needs to work
4) The unexpected secret to getting your first customers 5) The AI opportunity that most startups are overlooking
Transcript: https://www.madrona.com/databricks-ia40-ai-data-jonathan-frankle
Chapters:
(00:00) Introduction (01:02) The Vision Behind MosaicML (04:11) Expanding the Mission at Databricks (05:52) The Concept of Data Intelligence (07:42) Navigating the AI Hype Cycle (15:10) Lessons from Early Wins at MosaicML (20:50) Building a Strong AI Team (23:36) The Future of AI and Its Challenges (24:06) Evolving Roles in AI at Databricks (25:55) Bridging Research and Product (28:29) High School Track at NeurIPS (30:39) AI Techniques and Customer Needs (38:22) Rapid Fire Questions and Lessons Learned (42:49) Exciting Trends in AI and Robotics (45:40) AI Policy and Governance
4.8
1212 ratings
The biggest AI breakthroughs won’t come from Ph.D. labs — they’ll come from people solving real-world problems. So how do AI founders actually turn cutting-edge research into real products and scale them? In this week’s episode of Founded & Funded, Madrona Partner Jon Turow sat down with Jonathan Frankle, Chief AI Scientist at Databricks to talk about the shift from AI hype to real adoption — and what founders need to know.
They dive into:
1) How AI adoption has shifted from hype to real-world production
2) The #1 mistake AI startups make when trying to sell to enterprises
3) Why your AI system shouldn’t care if it’s RAG, fine-tuned, or RLHF — it just needs to work
4) The unexpected secret to getting your first customers 5) The AI opportunity that most startups are overlooking
Transcript: https://www.madrona.com/databricks-ia40-ai-data-jonathan-frankle
Chapters:
(00:00) Introduction (01:02) The Vision Behind MosaicML (04:11) Expanding the Mission at Databricks (05:52) The Concept of Data Intelligence (07:42) Navigating the AI Hype Cycle (15:10) Lessons from Early Wins at MosaicML (20:50) Building a Strong AI Team (23:36) The Future of AI and Its Challenges (24:06) Evolving Roles in AI at Databricks (25:55) Bridging Research and Product (28:29) High School Track at NeurIPS (30:39) AI Techniques and Customer Needs (38:22) Rapid Fire Questions and Lessons Learned (42:49) Exciting Trends in AI and Robotics (45:40) AI Policy and Governance
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