Dylan Patel

We think that OpenAI had a large margin built in. There's multiple factors.

All their low-level libraries that we talked about in training, some of them probably translate to inference, and those weren't released.

All their low-level libraries that we talked about in training, some of them probably translate to inference, and those weren't released.

All their low-level libraries that we talked about in training, some of them probably translate to inference, and those weren't released.

Some of the interviews, there's discussion on how like doing this as a recruiting tool. You see this at the American companies too. It's like, Having GPUs, recruiting tool. Being at the cutting edge of AI, recruiting tool. Open sourcing. Open sourcing, recruiting tool.

Some of the interviews, there's discussion on how like doing this as a recruiting tool. You see this at the American companies too. It's like, Having GPUs, recruiting tool. Being at the cutting edge of AI, recruiting tool. Open sourcing. Open sourcing, recruiting tool.

Some of the interviews, there's discussion on how like doing this as a recruiting tool. You see this at the American companies too. It's like, Having GPUs, recruiting tool. Being at the cutting edge of AI, recruiting tool. Open sourcing. Open sourcing, recruiting tool.

They released it on Inauguration Day. They know what is on the international calendar, but I don't expect them to. If you listen to their motivations for AI, it's like,

They released it on Inauguration Day. They know what is on the international calendar, but I don't expect them to. If you listen to their motivations for AI, it's like,

They released it on Inauguration Day. They know what is on the international calendar, but I don't expect them to. If you listen to their motivations for AI, it's like,

I think that's one of their big advantages. We know that a lot of the American companies are very invested in safety. And that is the central culture of a place like Anthropic. And I think Anthropic sounds like a wonderful place to work. But if safety is your number one goal, it takes way longer to get artifacts out. That's why Anthropic is not open sourcing things. That's their claims.

I think that's one of their big advantages. We know that a lot of the American companies are very invested in safety. And that is the central culture of a place like Anthropic. And I think Anthropic sounds like a wonderful place to work. But if safety is your number one goal, it takes way longer to get artifacts out. That's why Anthropic is not open sourcing things. That's their claims.

I think that's one of their big advantages. We know that a lot of the American companies are very invested in safety. And that is the central culture of a place like Anthropic. And I think Anthropic sounds like a wonderful place to work. But if safety is your number one goal, it takes way longer to get artifacts out. That's why Anthropic is not open sourcing things. That's their claims.

But there's reviews internally. Anthropic mentions things to international governments. There's been news of how Anthropic has done pre-release testing with the UK AI Safety Institute. All of these things add inertia to the process of getting things out. And we're on this trend line where the progress is very high.

But there's reviews internally. Anthropic mentions things to international governments. There's been news of how Anthropic has done pre-release testing with the UK AI Safety Institute. All of these things add inertia to the process of getting things out. And we're on this trend line where the progress is very high.

But there's reviews internally. Anthropic mentions things to international governments. There's been news of how Anthropic has done pre-release testing with the UK AI Safety Institute. All of these things add inertia to the process of getting things out. And we're on this trend line where the progress is very high.

So if you reduce the time from when your model is done training, you run evals, it's good. You want to get it out as soon as possible to maximize the perceived quality of your outputs. Deep Seat does this so well.

So if you reduce the time from when your model is done training, you run evals, it's good. You want to get it out as soon as possible to maximize the perceived quality of your outputs. Deep Seat does this so well.

So if you reduce the time from when your model is done training, you run evals, it's good. You want to get it out as soon as possible to maximize the perceived quality of your outputs. Deep Seat does this so well.

I mean, like people are infatuated with you. You're telling me this is a high value thing and it works and it's