Noam Lovinsky

Everyone is building the thing.

And you just have a small number of people that have their hands on a much wider part of the product pipeline.

And I think that leads to two better products.

Well, I mean, I think that for one, the AI can do a lot of the testing.

Certainly the, you know, the first run catching all the obvious things.

I mean, even for on-call like incidents, like when the kind of bad things happen, I think that AI can do a lot of the triage, a lot of the first run investigations so that by the time it gets to an on-call engineer, it's like, here's what I think is going on.

Here's, I think the three options are to try to fix this, like which one of these paths do I'm going to take go.

And I think over time, as you build up that, that context and that, and that memory, then, you know, it's going to ask you like less and less.

So I think that's,

the whole stack is going to be automated in the same way.

I think that maybe for our teams, the fundamental thing that can get shrunk is the exploration phase and the rate of iteration through the exploration phase, how quickly you can kind of get to this is the thing we actually need to build.

And we've learned that.

We've tested that.

We've iterated through that exploration phase.

That's shrunk quite significantly.

I don't know how to put like a, it's 2x, it's 3x on that.

But I do think that in the limit, that speeds you up quite dramatically because usually-

I think that the phase of going through, we've observed this problem, how do we build a solution for this problem?

Okay, let's try to iterate through what a solution might be.

Let's go and test that with some of our customers.