AI systems are getting more powerful, and they're increasingly being used to build the next version of themselves. We want to illuminate that progress for the public. Today, we're sharing three measurements that help track AI development: 1. How much AI R&D is done by AI. 2. How well AI agents are overseen. 3. How compute is allocated. We provide a snapshot of these metrics from inside Anthropic. Any frontier developer could publish the same measures, and third parties could verify them. As the world considers pacing the frontier, we should do everything possible to minimize the gap between what frontier labs know and what the public knows. This means better measuring the development of AI, publishing our findings, and giving society an opportunity to decide how to use this information. Read the full post and methodology:

Last month I wrote about how we can build a positive and safe future for everyone: Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens. The reality is: - People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned. There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind. - Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well. Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built. - Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators. - Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well. I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.

Muse is already getting more daily US downloads than Threads, WhatsApp and Facebook, and it's only 3,000 behind Instagram Biggest consumer AI launch since ChatGPT. A new tier 1 consumer app is born

Everyone is evaluating the agents without using the lens of voice communication. The reason it's easier to use Instinct is because it works via voice. Talking is cognitively easy vs opening your phone or app and being forced into some nonsense by some random shitty app developers. "Talking lets you dump intent while you're walking, driving, cooking, looking at something, or thinking through a problem."

We’re constantly running A/B tests in production with different AI models. Increasingly, we’re finding that across some of our core use cases, users simply can’t tell the difference between frontier models and models that are 50× cheaper. And this is happening more and more often.

$NVDA has been extremely aggressive lately, both in helping/saving the Neoclouds and in AI model development. They report earnings in a few days; it will be interesting to see if there are any cracks in the gross margin that are driving these aggressive moves.