Muse and team over the past weekend!

Muse > Dot

Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. https://nvda.ws/4hcoq7m

What happens to jobs, productivity, inequality, and economic policy as AI transforms the way we work? Anthropic Chief Economist @PeterMcCrory and economist @JasonFurman took to the JFK Jr. Forum stage to explore these questions and more. https://ken.sc/forum0923-live

just crossed four years at openai! the special thing about this place is the constant capacity for rebirth. for all its faults, there is nowhere quite like it. the team makes high conviction, contrarian bets over and over, and they are mostly right. it's a new company every three months. i have really enjoyed my part in some of the bets, and i'm excited to share some of the current ones we are cooking up now. onward!

A few (personal) thoughts on reading empirical AI papers on the economy. Economists have gotten used to reading papers with super clean identification, arguing about the validity of an instrument, making sure parallel trend assumptions are satisfied. This is what gets you into a top journal, and it is *very* important research (no question here). But it also takes years and sometimes decades to get these types of papers right---people often don't find a good instrument to answer a specific causal question decades after the natural experiment. We will eventually have this type of research for AI as well, and it is absolutely necessary. But right we also need signals *right now*, even if they are noisier than what we are used to. We need papers where we can trust that researchers did their best methodologically, while at the same time acknowledging that the space is moving way too fast to wait for perfect identification. This will allow us to accumulate enough signals, coming at the same question using different angles, for example, to say "yes, X is likely happening in the economy". The AI exposure and early career hiring papers are a good example of this. There is no silver bullet paper with super clean identification. But at this point we have several independent teams reaching the same general conclusion, enough where we can say "there seems to be a slow down in AI-exposed, early career hiring."