Nathan Gower (3:22)

Now, just in case you haven't been playing the stock market for the last 40 years, let's recap. 1987 saw the Black Monday crash. 1999 saw the dot com bubble which burst the following year. And 2009 was in the wake of the great financial crisis. Now the stock market never stands still. And since we spoke to Simon just a few weeks ago, it has continued to rise. That $28 trillion in added value is now about $30 trillion. But how much of this rise is down to AI?

Simon French (3:54)

Unfortunately, we don't put tech stocks in a neat bucket of what is AI and what is not AI. Are they true AI companies? It's very difficult to decipher. So if you just take the sector classification of technology, it's about half that.

Nathan Gower (4:07)

Addition of market capitalization to put that into numbers. Global stocks have added $30 trillion in value and about 12.5 trillion of that is in the technology sector, almost half the added value. But it's not just the scale of this increase in this short time that is raising eyebrows. There are other more sophisticated metrics we can look at.

Simon French (4:29)

Equity analysts, strategists will use a variety of measures to understand how expensive stock markets are. One of the most widely used is the Shiller price earnings rat, which controls the fact that earnings sometimes in companies are volatile. And that measure is hovering around 40 times earnings in the US stock market. Now it's only been there once before in modern market history and that was at the height of the dot com bubble in 99 early 2000.

Nathan Gower (4:56)

We've mentioned the dot com bubble a few times now. It's a moment from history that often comes up in discussions about a potential AI bubble. Katie Martin is a markets columnist at the Financial Times.

Katie Martin (5:08)

There was a period of time where, you know, as Simon was suggesting, there was, you know, the Internet was born. Hooray, we've got this bouncing baby Internet. And there was a bunch of stocks that were connected with it. And there are some of them that have, you know, survived through to the end. You look at, you know, you look at Amazon for example, that's the kind of prime example of the company that sprang up in this era and has become this multi trillion dollar behemoth. But what you also had along the way is a whole series of stocks that reached out absolutely crazy valuations. So stock investors were saying, we think this particular stock is worth Hundreds of multiples of the amount of money that the company is actually making. The prime example that people always bring up here is pets.com and, and so there was this cluster of stocks that were worth just well in excess of the money that they were actually making. And inevitably that ran out of road and they all crashed all at once. And that left a really significant mark on US stock markets. It took years for people to, if they had invested at the top. It took years for people to come back to being whole.

Nathan Gower (6:15)

Okay, back to the present. It's undoubtedly true that the US stock market has had a very strong year so far. But Katie Martin thinks there's more to it than meets the eye.

Katie Martin (6:26)

One of the really important things to bear in mind here is that the main index that everyone talks about when they talk about US stocks is the S&P 500 and that is weighted, weighted according to the size of the companies inside it. Now you know, wrap your head around this number. Nvidia is worth, last time I checked, 4.4 trillion with a T dollars. That is a chunk of change. Yeah. One of the things that people really worry about in relation to the AI trade is we are talking about a tiny number of companies. You know, people talk about the magnificent seven, the tech stocks in the US seven, just seven. You know, there's 500 stocks more or less in the S&P 500. So there's a lot of companies that are having a stinker of a year. But the index is being held up. You know, on top of these companies like Apple which is having a great run. Nvidia, which is just, just too big to even wrap your head around in the chips industry. So it is very, very concentrated. And to me that means you have quite a small number of potential points of failure. Which worries me somewhat.

Nathan Gower (7:31)

That Magnificent Seven that Katie Martin mentions are the seven huge tech giants which dominate the global tech scene. You've got Apple, Amazon, Microsoft, Nvidia, Alphabet, Meta and Tesla. Some people argue that if AI turns out not to be as profitable as hoped, many of these established companies have broad shoulders and earn huge amounts of cash from other areas. And that reduces the likelihood of, of a bad crash in value. But not all tech companies are so lucky. Simon French Again, another interesting data point.

Simon French (8:06)

Is the rally that we've seen in the US stock market. While a lot of the focus comes on those big cash generative Magnificent Seven stocks, the biggest rally since April is amongst non profitable technology companies, those that don't make any earnings at the moment. And that has uncomfortable parallels with the dot com era, where that was really those, those companies were the poster child of some of those ones that had what were very, very elevated valuations that triggered the bubble bursting.

Nathan Gower (8:38)

So let's go back to our listeners question, can we identify bubbles before they burst?

Simon French (8:44)

So listeners should never think that any of us, however senior we are, Jamie Dimon, Andrew Bailey, Jerome Powell, none of us really know how to time the market. The best investors of all time acknowledge that the greatest economist of all time, John Wayne, said the market can remain irrational longer than I can remain solvent. And that is an important message to remember. The point I would make is you try and calibrate. You calibrate, are there more risky signals out there or less risky signals? And you calibrate how much risk you would take accordingly. I would say on the balance, there are more risky signals than I've seen for quite a long time in the stock market. So I'd be more cautious in that environment.

Nathan Gower (9:28)

Our thanks to Simon French and Katie Martin. That's all we've got time for this week. And remember, if, like Yanni, you've got a burning question that you'd like to outsource the research for, just drop us a line at more or lessbc.co.uk. please give us something to do. Lizzie is just working on her side project of counting all the prime numbers.

Simon French (9:47)

Up to a million 694,193, 694,201.

Nathan Gower (9:54)

Tune in next week to see if she's finished. If you want to pass the time, try listening to the briefing room on BBC Sounds. Until then, goodbye.

Ray Winstone (10:09)

Hello, it's Ray Winstone. I'm here to tell you about my podcast on BBC Radio 4, History's Toughest Heroes. I've got stories about the pioneers, the rebels, the outcasts who define tough. And that was the first time that anybody ever ran a car up that fast with no tires on. It almost feels like your eyeballs are going to come out of your head. Tough enough for you? Subscribe to History's Toughest Heroes. Wherever you get your podcast.