Google & Meta AI Researcher Debunks “AI Will Take Your Job” Narrative

Jack Morris is a PhD student in Computer Science at Cornell Tech and an NSF GRFP fellow, with a research focus on cutting-edge AI systems. Before diving into academia, Jack completed a prestigious AI Residency at Google, where he worked on real-world machine learning challenges. With a dual background in math and computer science from the University of Virginia, Jack brings both theoretical depth and practical experience to the evolving world of AI.

Follow Jack: twitter: x.com/jxmnop & substack: blog.jxmo.io


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Show hosts Joe (Eng VP) and Jordan (M&A Deal Lead) worked at various companies from Google, Apple, Facebook, Microsoft, Salesforce, Slack, Carta, Splunk, Wealthfront, Adobe, and more.

00:00 - AI's Potential Through Screen Data
00:51 - Automating Tedious Tasks to Boost Productivity
01:16 - Guest Introduction and Background
02:05 - Is AI Research Progress Slowing or Shifting?
04:27 - Research Paper Volume vs. Research Impact
06:14 - Expanding vs. Filling Research Space
07:33 - How Breakthroughs Like Transformers Expand the Field
09:27 - Citation Counts vs. Real Influence in AI
11:01 - Why Big Problems Are Hard to Tackle
13:13 - Challenges of Competing in Crowded Research Areas
15:23 - How Researchers Triage and Prioritize New Papers
17:28 - Are Research Papers Becoming Less Impactful?
20:28 - What Are Research Papers Actually For?
23:14 - Overreaching Claims in Research Examples
25:33 - What Makes a Paper Open New Areas of Discovery?
28:16 - Examples of Territory-Expanding Research
30:25 - How to Spot High-Impact Ideas
33:30 - Misconceptions About Corporate AI Research Labs
36:02 - Large Companies Funding Niche AI Science
38:07 - Why LLMs Have Taken Over AI Research
41:19 - How LLM Infrastructure Benefits All of AI
44:09 - The Legacy of Underappreciated Research
45:47 - Can AI Really Replace Most Jobs?
48:33 - Why Radiology Still Needs Humans
50:01 - Automating Workplace Communication and Scheduling
52:28 - Recording Yourself to Train a Personalized AI
52:57 - Memory and Reasoning in Future AI Systems
55:02 - Techniques to Extend Context and Memory
56:28 - Predicting Personal AI Tools Within 3 Years
57:24 - Final Thoughts and Closing