Manu Sharma, founder and CEO of Labelbox, explains how frontier AI training data has evolved far beyond simple labeling to sophisticated reinforcement learning environments where domain experts create "gyms" for models to develop complex skills.

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Manu Sharma, founder and CEO of Labelbox, explains how frontier AI training data has evolved far beyond simple labeling to sophisticated reinforcement learning environments where domain experts create "gyms" for models to develop complex skills. With every Western frontier lab now spending over a billion dollars annually on training data, the conversation traces the shift from supervised learning to reinforcement learning from verifiable rewards, particularly for coding, mathematical reasoning, and computer use. Sharma reveals how Labelbox operates as a vertically integrated data factory, conducting over 2,000 AI-powered expert interviews daily and paying top specialists more than $250,000 annually. The discussion provides essential insights into the red-hot training data market that's reshaping AI development following major deals like Meta's $15B acquisition of Scale AI.

Sponsors:
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The AGNTCY: The AGNTCY is an open-source collective dedicated to building the Internet of Agents, enabling AI agents to communicate and collaborate seamlessly across frameworks. Join a community of engineers focused on high-quality multi-agent software and support the initiative at https://agntcy.org

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CHAPTERS:
(00:00) About the Episode
(03:23) Introduction and Industry Chaos
(04:25) AGI Race Components
(11:09) Post-Training Evolution (Part 1)
(11:15) Sponsors: Oracle Cloud Infrastructure | The AGNTCY
(13:15) Post-Training Evolution (Part 2)
(15:28) Compute Budget Shifts
(23:31) Human Data's Role
(25:35) Expert Data Importance
(31:52) Training Paradigm Shift (Part 1)
(31:57) Sponsor: NetSuite by Oracle
(33:21) Training Paradigm Shift (Part 2)
(34:41) Solution Evaluation Framework
(36:48) Long Context Challenges
(38:37) Testing Long Context
(42:17) Data Collection Evolution
(43:41) Fine-Tuning vs Context
(49:55) Context Engineering Dominance
(56:39) Popular Fine-Tuning Models
(57:43) Context Engineering Coaching
(01:03:29) Creative vs Automated
(01:06:32) Frontier vs Enterprise
(01:12:54) Enterprise Implementation Support
(01:15:29) Sovereign AI Strategy
(01:24:24) Computer Use Data
(01:28:08) Generalist Data Contributors
(01:29:05) AI Interviewing Lessons
(01:34:02) Industry Future Outlook
(01:38:30) AGI vs Superintelligence
(01:41:02) Closing Thoughts
(01:41:15) Outro

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