Azeem Azhar

I think it was 92% of their technical staff using those code generation tools every day.

Now, of course, Coinbase is a relatively young, dynamic company, so it's natural that it would be an early adopter.

So these early adopters are, of course, demonstrating some concrete gains.

But I read something in the Wall Street Journal this week

which was really quite surprising.

It looked at BNY, which is a bank in the US, and Walmart, and came up with a couple of quite strong and surprising examples of how they were using AI and getting some kinds of results.

So in the case of BNY, which used to be called Bank of New York, I think, years ago, that's how I remember it,

They've deployed a hundred digital employees who have their own login credentials and can go in and get work done using the internal systems that human employees are using.

With their own login credentials, they can access the systems they need.

BNY claim this is actually giving them bottom line impacts through growing the capacity to do work.

One example they talk about is a digital engineer that can scan code for vulnerabilities and implement fixes as they get found.

So that's quite an interesting example because, of course, on the one hand, you are just saving the human dollars of doing that.

But on the other hand, because the digital engineers can work

ceaselessly and constantly, 24 hours a day for their few joules of energy, you might actually identify and close vulnerabilities faster than otherwise, which could save you from some kind of horrible cyber effect.

The Walmart example is pretty clever as well.

It's the trend to product agent.

So Walmart claims that what this has done is shorten fashion production timelines

from six months to eight or nine weeks.

So again, that matters in this world of fast-moving cultural trends.

There is a trend, it's emerging on TikTok or somewhere else, and you want to get the appropriate garment in the stores as quickly as you can.