Nathan Lambert is a Senior Research Scientist and Post-Training Lead at the Allen Institute for AI (Ai2), where he leads open-source language model development on the OLMo and Tulu series. A UC Berkeley PhD, he previously led the RLHF team at Hugging Face, co-building the TRL library and the Zephyr model. He runs Interconnects AI, a Substack newsletter read by tens of thousands covering post-training, open models, and AI policy, and is the author of The RLHF Book (Manning Publications). With roughly 8,000 academic citations and a reputation for demystifying the hardest parts of modern AI, Lambert is one of the most trusted voices at the intersection of open-source AI research and public education.
Predibase was a San Francisco-based AI infrastructure company (founded 2020, acquired by Rubrik in June 2025) that pioneered efficient LLM fine-tuning and serving at scale. Built by the creators of Uber AI's Ludwig and Horovod frameworks, Predibase made it easy for enterprises to fine-tune and deploy open-source LLMs using LoRA adapters — often outperforming GPT-4 on domain-specific tasks for under $8 of compute. Its open-source LoRAX inference server enabled serving thousands of fine-tuned models from a single GPU, dramatically cutting costs. After raising $28M from Greylock and Felicis, Predibase was acquired by cybersecurity firm Rubrik for over $100M to accelerate agentic AI adoption.
Scale AI is a San Francisco-based AI infrastructure company founded in 2016 by Alexandr Wang and Lucy Guo. It provides the data engine, evaluation tools, and AI deployment platforms that power the world's leading AI labs, Fortune 500 enterprises, and US government agencies. By combining a massive distributed workforce with proprietary tooling, Scale accelerates AI development through high-quality data labeling, RLHF, model evaluation, and agentic platforms — making it one of the most consequential picks-and-shovels companies in the modern AI boom, with a $29B valuation as of mid-2025.