The Two Crises That Almost Killed ChatGPT

Before ChatGPT answered the public, engineers fought two existential problems: training collapse and confident falsehoods. This video breaks down how scale-induced gradient explosions nearly wrecked weeks of compute — and how overconfident “hallucinations” threatened trust. Watch a clear, fast-paced explainer on gradient clipping, normalization, monitoring, RLHF, calibration, and leadership decisions that prioritized safety over hype. Perfect for AI-curious viewers who want the real story behind stability, honesty, and the tradeoffs that shaped ChatGPT. Like and share if you found this peek behind the curtain useful — I’ll follow up with a deep dive on collapse-detection tools and a short clip focused on RLHF. #ChatGPT #AI #MachineLearning #RLHF #ModelCollapse

OUTLINE:
00:00:00 HOOK
00:00:15 SETUP — WHAT WE'RE DEALING WITH
00:00:43 PROBLEM 1 — TRAINING INSTABILITY (THE CRASH)
00:01:17 TECH SKETCH — WHY IT HAPPENED (HIGH-LEVEL)
00:01:42 PROBLEM 2 — CONFIDENCE TOO EARLY (THE LIE)
00:02:17 WHY SOLVING BOTH WAS HARD
00:02:44 HOW ENGINEERS FOUGHT BACK — STABILITY SIDE
00:03:10 HOW ENGINEERS FOUGHT BACK — TRUST SIDE
00:03:49 LEADERSHIP & COST — SAM’S ROLE (DECISION MAKING)
00:04:16 TRADEOFFS & LESSONS
00:04:38 CLOSE & CTA