A product discussed on Latent Space.

Outlasting Noam Shazeer, Crowdsourcing Chai AI w/ 1.4m DAU — with William Beauchamp, Chai Research
Jan 26, 2025 · 1:13:33
William Beauchamp, founder-CEO of Chai AI, explains how he pivoted from algorithmic trading to build a character chatbot platform before Character.ai, growing to 1.4M DAU and $22M+ revenue by crowdsourcing model improvements through Chaiverse. Starting with GPT-J in 2021, he found product-market fit with a therapist bot and shifted to user-generated content, letting users define prompts, images, and names. Competing against well-funded rivals like Character AI and Talkie, Chai ships over 100 LLMs weekly via its developer platform, spending $10M on compute in 2024 and tripling it. Beauchamp argues AI follows an S-curve, not scaling laws, and focuses on inference optimization using rejection sampling and reward models to serve better responses. He prioritizes 'insanely great' products over technology-driven features, noting that audio and image features failed to move metrics, while a data flywheel and aggressive user acquisition drove rapid growth.

[Paper Club] 🍓 On Reasoning: Q-STaR and Friends!
Sep 18, 2024 · 47:12
This episode of Paper Club surveys three reasoning papers — STaR, Quiet-STaR, and V-STaR — arguing that while STaR is foundational for bootstrapping reasoning via rationales and rationalization, Quiet-STaR's attempt to generate rationales at every token yields only marginal gains (5–10% on GSM8K and CQA), and V-STaR's verifier trained with DPO on both correct and incorrect solutions delivers the most practical improvement, beating majority voting. The host explains STaR's two-loop process of generating rationales and rationalizations from wrong answers, highlights examples like a filtering-straw question where human raters evaluated reasoning quality, and notes that STaR on GPT-J 6B achieved human-like step counts in math problems. V-STaR's verifier selects among candidate solutions, scaling with K candidates, and is compared to process reward models from OpenAI's "let's verify step by step."
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