Dylan Patel

Yes, there's a lot of stuff that, you know, the image recognition model does, but we just also look at them a lot.

And then it's like compiling them and selling a spreadsheet that you get biweekly reports on like all the data centers or what's changed or like, hey, actually this Amazon data center, the fans are starting to spin.

So actually there's revenue going on from this Amazon data center so we can forecast Amazon's revenue.

It's like, oh, okay, like this is relevant.

I don't think this would have been possible just a few years ago.

There's demand for it because everyone wants to track this and it's so important, but it's like, it begets each other.

And I think like, at least in my daily life, I was like, I don't think I could have taken that step from where I was in a business, which was still a research provider, but like that is a monumental jump.

And like being able to do it with three people out of the gate versus like 50 or a hundred, like, I don't know how many people would have taken, but I don't think it's possible.

And it's like mainframe migration is something people have always wanted to do.

Amazon leaving Oracle took fucking 20 years and they wanted to do it 20 years ago and they had their highest revenue products after EC2.

The next four were like database products at AWS and yet they still freaking used Oracle's database because it's hard.

Mainframe migration can be way faster or like migration from one tech stack to another can be way faster.

You can make your business more efficient.

You add more automation.

I think as far as like, yes, the tech exists, go to all the businesses around the world.

And it's like, they aren't using the leading edge of what they could.

They aren't using what a 2020 company could have done without AI.

No one is doing that.

And if they did, they'd be so much more efficient.

All of these things just take too long to build.