THIS IS A MASTERCLASS ON HOW AI IS BEING TRAINED AT OPENAI AND ANTHROPIC.

Brian Roemmele posted a thread titled „THIS IS A MASTERCLASS ON HOW AI IS BEING TRAINED AT OPENAI AND ANTHROPIC.“ In it, he invites readers to take roughly 30 minutes to see what he’s seen, and he warns that the piece exposes mistakes some so-called experts helped create. It reads like a guided tour through the mechanics and trade-offs behind modern model training, and it’s worth a careful look.

What Roemmele highlights is familiar ground to practitioners, but framed in a way that makes the consequences clearer for everyone. He walks through how models are fed data, how human feedback is used (sometimes imperfectly), and how small choices during training ripple into real-world behavior. He points to hidden assumptions, incentives that steer design, and subtle failure modes that often show up only after wide deployment. Yes, it’s technical in parts, but the core message is urgent: the way systems are trained shapes the world they produce.

A bit of context helps. Modern large models evolved from simple supervised learning to more layered pipelines that include human labeling, reinforcement signals, and safety filters. That complexity brought big gains, but also new blind spots. Roemmele’s thread acts like a field guide to those blind spots, and it nudges toward fixes: more transparency, better audit practices, and training incentives aligned with long-term safety instead of short-term metrics.

Practical takeaway, from his perspective: teams should bake in continuous audits, diversify training sources, and treat human feedback as fallible data that needs its own checks. Small experiments, repeated often, reduce surprise.

Read the original thread on X: https://x.com/BrianRoemmele/status/2012326257740968081

If the community pays attention, these lessons could steer development toward safer, more useful systems. It’s a little messy, sometimes uncomfortable, but ultimately hopeful—because better design choices are still within reach.