Regarding the last topic @DavidSacks : With respect, the data being used to train frontier models in the U.S. is not a commodity. It is American intelligence, paired with anonymized operational data from U.S. enterprises. Put simply, we have top doctors, scientists, lawyers, and physicists in the U.S. using their knowledge to create detailed rubrics that train these models. Each individual data point and its corresponding rubric can take an expert anywhere from 10 to 30 hours to create. This is not preference labeling or drawing bounding boxes. It is highly complex, structured human judgment from leading experts here in the West—people who deeply understand and directly contribute to the latest American innovations in their respective fields. That expertise is then converted through highly specific data structures and RL environments (developed collaboratively by U.S. AI labs and data labs) from raw human intelligence into high-signal rewards that improve frontier models. On top of that, any AI advancement, even something that begins as a simple chatbot designed to improve operations within a defense agency, can create a major competitive advantage in adversarial situations and may have dual-use applications. Lastly, many datasets today are seeded with anonymized, real-world operational data from U.S. companies to build highly realistic environments. When those datasets are sold to China, we are not simply exporting “labeling.” We are exporting proprietary American intelligence, structured for machine learning and delivered directly to China at scale. It is very easy to categorize this work as “labeling” and ignore what it actually represents. However, as @altcap suggested: “Then these things will get a lot more scrutiny than they’re getting today. I think the only reason they pass muster today is because we’re still leading the race.” If China catches up to U.S. labs, this will become much harder to ignore. In retrospect, the role of data as the root cause will become very clear — and by that point, it may be too late. The race is tight. I suggest looking into this now.