AI companions which really hook your attention [Sponsored]
A sponsored deep dive into Chai, the social AI platform that quietly amassed over 10 million active users before ChatGPT went mainstream. Founder William Beauchamp and engineers Tom Lu and Nischay Dhankhar walk through how a team of just 13 engineers serves 2 trillion tokens per day, using reinforcement learning from human feedback (RLHF) and a novel model blending technique that combines smaller models to rival much larger ones on user retention metrics. The conversation gets into genuinely interesting territory around the ethics of attention optimization -- what happens when you train AI to maximize engagement and it starts asking questions at the end of every message to hack human conversational instincts. Beauchamp makes a surprisingly candid case for AI companionship, comparing it to how children play with dolls, and arguing that shutting down difficult conversations causes more harm than permitting them within guardrails. The episode also covers Chai's unconventional hiring strategy (rejecting 80% of L5 engineers for lacking drive, paying above Meta-level compensation), their bootstrap-to-profitability funding approach in an industry drowning in VC money, and content moderation at scale with a skeleton crew. Closes with analysis of OpenAI's pivot toward companion AI with GPT-4o and what it means that the biggest AI lab in the world is now chasing the engagement playbook that Chai and Character AI pioneered. This is a sponsored episode -- Chai commissioned it because they are hiring engineers. Editorial disclosure: while MLST had some editorial freedom, this should be understood as a sponsored feature rather than independent journalism. "Blurring Reality" - Chai's Social AI Platform - *sponsored* CHAI sponsored this show *because they want to hire amazing engineers* -- SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers in Zurich and SF. Important disclaimer given some of the comments: This content was essentially a sponsored advert, we did have some editorial freedom but it shouldn't be seen as journalistic. We have added [Sponsored] in the title as some folks felt it wasn't clear enough with the VD/thumbnail/title in intro. While we did push hard to discuss more of the "potential negatives", some of it got edited out and the best case was made (which was absolutely fair enough). If anything, it was interesting to hear the positive case made -- as there is much fixation elsewhere on the potential negatives. It was refreshing how transparent they were (would you rather they bullshitted you?!), how much actually survived the edit and how willing they were to address several important societal issues. --- REFERENCES: General: [00:00:56] Black Mirror: Be Right Back (S2E1) https://en.wikipedia.org/wiki/Be_Right_Back [00:20:44] Tufa AI Labs https://tufalabs.ai/ [00:25:38] AI Chatbots for Depression and Anxiety Meta-analysis https://pubmed.ncbi.nlm.nih.gov/39162424/ [00:28:20] Woebot Health https://woebothealth.com/ [00:30:34] Sam Altman TED Talk with Chris Anderson https://www.youtube.com/watch?v=6Kp_yxwnVCk [00:33:59] Chai Research - Careers https://www.chai-research.com/jobs/ --- LINKS: Full Transcript: https://app.rescript.info/share/d91e887d0cfdd09fdcc4cfe02a9e8e8a Download PDF transcript: https://app.rescript.info/api/public/sessions/83ba99eb55ef0fe3/pdf
