Nathaniel Whittemore

This, by the way, is also some of the new features that we saw coming into Codex in that episode from a couple of days ago.

Importantly, employees don't actually have to be on their devices for this to happen.

There's a bunch more, but I thought that this conversation about why they took the time to build this might be relevant for those of you organizational listeners who are trying to decide whether some version of this type of effort is worth it for you.

Seb writes, the obvious question is why not just buy this, and argues that there are three reasons that they built it in-house.

One, he writes, internal productivity is a moat.

Using AI well is now a core business need.

The companies that make every employee effective with AI will move faster, serve customers better, and compound advantages their competitors cannot match.

That makes internal AI infrastructure part of your moat, and you do not need to hand your moat to a vendor.

Two, speed.

When you own the tool, you see exactly where people get stuck.

You can ship fixes the same day someone reports a problem.

We have a Slack channel where users report issues and our team triages them into tickets automatically, with most resolved in hours.

You cannot do that while waiting on a vendor's roadmap.

Three, it directly informs our external product.

Ramp is an AI-first company building products for finance teams, and many of the problems we solve for internal users translate directly to customers.

How do you build memory that actually helps?

How do you enable people to build, distribute, and maintain effective skills?

How do you surface functionality through usage?

Solving these problems internally gives us conviction about what works before we ship it.

Glass gives us reps on the hardest AI product problems without those reps happening at customer's expense.