Anjney Midha

In a poetic sense, in a historical sense, if you think about the Wild West or the Western frontier, it wasn't just one frontier.

There was a frontier of gold and there was a frontier of jeans.

It turns out Levi's turned out to be a new modern behemoth of a company.

I mean, there were so many new businesses founded in the industrial revolution.

And I think that's the reality is the software engineering frontier, which is where Anthropic is clearly leader, is one frontier.

I think the chat frontier, the sort of consumer chat frontier is another frontier where OpenAI has been a leader.

And so I think there's just many, many frontiers to be conquered, or pioneered, rather.

I think Anthropic is clearly a role model for the rest of the community on how to do it in an efficient way.

There are, I think, fewer than 5,000 people, and they've been able to put out state-of-the-art models that teams like Google, which have 60,000 people,

are close to, but not yet quite there.

So actually, I don't really agree with your assessment that they're all at parity.

If you use the models day in and day out, they're quite remarkably different in meaningful ways to the person with hands on the keyboard doing the engineering work.

And I think those differences reflect the focus of the teams, right?

What is the actual mission that the team working on that domain cares about day after day after day?

So in the Stanford class I teach, the first lecture was a breakdown of how frontier models are even created.

And it's actually quite simple.

The recipe is super simple.

There's basically four steps.

There's pre-training, mid-training, post-training, and then what we call the continuous feedback loop.

So pre-training just says, hey, you collect a bunch of data from the internet and train a model to be a generally good pattern recognition machine.