Jensen Huang pushed back hard against recent extinction risk warnings from Anthropic researchers, calling the 10%+ prediction "made up" and "irresponsible." He argued these forecasts lack scientific grounding despite coming from credentialed researchers. To make his point, Huang cited a string of failed AI predictions: radiologists were supposed to disappear within five years (instead we need more), 90% of code was supposed to be AI-generated within 6-12 months (didn't happen), and 50% of entry-level jobs were supposed to vanish in 6-9 months (also wrong). He acknowledged Jacob Coxon's whistleblowing deserved serious consideration but separated that legitimate concern from what he saw as unfounded doomsaying about extinction risk. On safety and engineering, Huang argued frontier labs have the competence to handle their own problems through better engineering practices. He referenced "four incidents from one lab" and "one giant incident from the other," suggesting these were solvable through root cause analysis and better tools like sandboxes, runtimes, and monitoring systems. Huang expressed confidence the labs already have "much better technology now" and would fix these issues internally rather than claiming they couldn't control their own systems. He dismissed the notion that outside help would be needed, betting "money that in every single one of those cases it's within their control in the future to prevent it." On the technical front, Huang explained that test-time compute and recursive self-improvement (RSI) are sensible approaches but require proper evaluation before releasing products. He noted RSI combines in-context learning, reinforcement learning, and synthetic data generation—techniques that improve AI performance over time without necessarily retraining base models. The key distinction: companies can experiment internally, but they must still verify and test before shipping. Huang also defended both closed and open models as necessary, pointing out that 80% of the $400 billion in AI venture funding over six months went to companies using open models, which he saw as essential for innovation diversity and sovereignty.