Azeem Azhar

It's also true that they're starting to see results pretty quickly and that many of them are struggling with it.

But that doesn't mean that getting this technology, getting that imaginary all-powerful AI into our economy, into our hands, across the whole of the US and beyond, will be as simple as pushing an update to an iPhone.

And we've got quite a few steps before we get there.

And what I start to see when I talk to

business people.

I was in Las Vegas a few weeks ago, and I did a show of hands with 300 IT bosses.

And half of them said, listen, we're not getting results now, but we should get results in a couple of years.

And when you talk to them, it's really challenging, practical stuff that is not far off trying to unblock a sink.

Because companies are difficult.

You don't just drop chat GPT and

Costco or Walmart and snap your fingers.

It takes time.

There's a lot to do.

And I think that that has been part of the story of how rapidly technologies diffuse and deploy across economies.

And so I take a view that these systems will get better, they'll get better quicker, that we have to get prepared for them to be really good.

But reality is always more messy than the spreadsheet model.

I think it's quite different to the dot-com bubble, because while there was a lot of circular supplier sub-financing, that was provided as debt rather than equity, which has technically a different categorization, and the spending was often sham on the way back, and nobody was using those fibers.

I mean, we didn't start to use those fibers that were laid until 2012, 2013, when YouTube was really, really kicking off.

So the differences here are that this is done in equity.

A lot of these things are tranched.