Yeah, please. Well, thanks, Pablo. Can I add my great thanks to the team from Thailand, Governor Ratanak and all his colleagues. We've really had an excellent meeting and an excellent time here. It's been a real pleasure. So yes, AI and technology and monetary policy. I mean, I want to start the sort of inner economic historian in me. It doesn't take long for it to come out. You know, I do think that the things over history that have actually raised, you know, potential growth rates over a sustained period are big changes, particularly in general purpose technology and the effects that that can have in terms of spreading across the economy.
And I think it is possible that one of the causes of us having lower potential growth rates in many of our economies for the last 15, 20 years is actually, you know, that we may have been between a period of general purpose technology innovation, with the previous one, sort of ICT and the internet, having sort of, you know, had its effects on growth rates, and then, you know, it's leveled off. And so you ask the question, what's the next one? Well, I think it's most likely some combination of AI and robotics.
And if you then, as you said, in the context of monetary policy, then if you think, well, how do we, you know, how do we go about thinking about this? I mean, if it's going to raise, you know, productivity levels and raise potential growth rates, I think the first thing to say is we're going to get demand effects and supply effects. And the question is, we do have to disentangle those two. And we have to disentangle them both in terms of their scale, but also the sequencing as well. And there's an excellent paper by Sana and some colleagues at the Bank of England that came out a month ago, which I think does just that, and I strongly recommend it as a sort of framework for thinking about these things.
I think at the moment, and of course it does differ from economy to economy, because clearly, you know, the US is in a different position in that respect, but I think we are seeing, you know, demand effects probably coming through more quickly, particularly via asset markets and asset valuations. And so I think that's probably more so in the US than the rest of us, but I think, you know, that is the mechanism that's coming through probably sooner. I think that, and obviously we're seeing, you know, quite strong asset valuations in that particular sort of sector, and that leads me to the question, well, you know, what could go wrong and what could change those effects and then feed through into those demand effects? And this relates to the supply effects as well.
And I think four things that I would have sort of, yeah, I have in my mind. So the first one is that obviously, you know, the demand effects, insofar as they come via asset prices, rest on markets forming a view on what the eventual productivity effects are going to be and what the effects on potential growth are going to be. Clearly, clearly markets could suffer a correction if they change their view on what those are. Obviously, it could go either way, by the way, but it could. The second one comes back to my point about timing. I think again, if you look back at history, it's typically taken longer for these general purpose technology effects to come through than people assume they will.
Now, you know, history doesn't all repeat itself, but there's every reason to think that, you know, in the age we live in now, these timing effects will be faster. But you go back, I mean, obviously you go back to Bob Solow's famous comment about you can see computers everywhere except in the productivity statistics. You go back to that, it was about, I don't know, 40 years from Edison wiring a light bulb up to it appearing in productivity numbers. So I think timing effects are important, and that's because a lot of the general purpose technology effects to come through require what I might call secondary innovation, if you like, to actually put it to work.
Third thing is, you know, you look at the markets, everybody is priced to be a winner at the moment. And of course, again, typically when you look back, everybody isn't a winner. You know, I always quote at this point, remember Netscape, and, you know, ask where Netscape is today, it isn't. So, you know, everybody doesn't win, and of course there are companies we've not even heard of who will eventually come through. So, you know, you will get sort of what I call churn effects. And then the fourth one is the one that for me is sort of newest in my thinking anyway, but I think is actually quite critical in the world we're in now. These asset valuations are now increasing, and obviously there's a lot of borrowing now behind this to fund the capital investment going on.
They are increasingly dependent upon public trust in the technology and particularly public trust in, you know, in AI. And let's face it, there are, you know, there are questions around that public trust. I think, you know, if you, this is a bit, get a bit philosophical for a second, if you try to sort of strip that down, I actually think at root it's about, you know, how much human control do we have over this thing and how much do we have relative to what we want to have.
And again, I think if that public trust was undermined, and let's face it, you know, Chris and I were on a panel earlier this week, you were quoting the French refrigeration situation. There was a headline I was quoting that morning from Anthropic saying that, you know, they can see places where AI agents could worm their way into much greater parts of our lives, as it were. You know, that public trust clearly is to a degree fragile, and that could affect, again, you know, the asset valuation demand effect channel. So those are the things that I look at. I think, you know, demand effects, you know, not surprised, we're probably seeing them faster than supply effects. I'd say that's backed up by history, and there are some fragilities.
