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The Macro Brief – Three eye-opening AI stats
1 Plays2 seconds ago
Alastair Pinder, Head EM and Global Equity Strategist, explores how artificial intelligence is driving stock markets to record highs - even amidst a global energy shock.
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Transcript
AI's Economic Impact on S&P 500
00:00:00
Speaker
We've been kind of estimating that this year alone, AI, in terms of the productivity improvement and the amount of costs it could save, could be in the region of $100 billion dollars for 500. So it's huge numbers.
Introduction: AI's Influence on Global Markets
00:00:21
Speaker
Hello, I'm Aline Van Dyne in New York, and welcome to the Macro Brief from HSBC Global Investment Research, the podcast that looks at the issues driving global financial markets.
00:00:33
Speaker
Today, we're looking at AI and how artificial intelligence is driving stock markets around the world, even amidst a global energy shock.
AI and Tech Stock Growth
00:00:43
Speaker
To do this, I'm joined in our New York studio by Alistair Pinder, our head of Emerging Markets and Global Equity Strategy. And we're delving into three eye-watering numbers that will help us ask whether the AI reality can live up to the hype.
00:01:00
Speaker
So welcome, Alistair. Thank you very much for having me. So let's kick off with earnings. We've had
AI's Wider Sector Impact
00:01:06
Speaker
a lot of earnings, of course, for the first quarter of 2026. For the Magnificent Seven, so these large seven tech stocks in the US, earnings growth in Q1 was over 60% on year.
00:01:20
Speaker
That sounds quite impressive. I mean, it's incredibly impressive. And one of the the most surprising things from this earnings season has just been how strong earnings have been. We've had almost 90% of companies report the earnings for Q1.
00:01:38
Speaker
At the beginning of the quarter, the expectation was that earnings would grow around 14% year on year. That's been closer now to 27%. And if you take the MAG7 specifically, the expectation for them was that their earnings would grow roughly 20%. And they just absolutely sort of smashed it with earnings growth, as you mentioned, of above 60%. So in this environment where you know investors have still been very concerned about the Middle East conflict, the energy shock, know the backdrop to all of this is that the S&P 500 is still just marching on and delivering fantastic earnings results.
00:02:16
Speaker
So the results are broader than
AI's Unseen Productivity Benefits
00:02:18
Speaker
just the tech sector. How much of that is in any way related to AI? So, I mean, it's a good question. i mean, you know, it is slightly broader than tech, but it is also quite confined to specific sectors. So really the the main drivers of the earnings growth has been tech, which is very much AI related. energy and materials, which is really as a result of higher commodity prices and and energy prices and financials um and and and partly driven by banks, which have benefited a little bit from the volatility in in equity markets globally. So there's a combination of factors that have driven it. But I think, you know, across the board, I would say the most important driver is AI at this point. And going forward, it is likely to continue to be the most important factor. And that's not just for tech stocks, but actually for non-tech stocks, what we also think is going to start materializing, and we're seeing more and more evidence of this, is companies using AI in a way to improve their productivity.
00:03:23
Speaker
And you know the most fascinating thing for me is that if you look at profit margins for companies stripping out tech, stripping out energy, stripping out materials, profit margins are continuing to accelerate and beat expectations. And to me, this is ah a classic sign of a productivity improvement on the way. So this is obviously really important because it's not just we're factoring in future gains. ah It's not just how much capital is is being invested in, which is something we'll delve into in a minute in more detail, but it's also productivity that's already being seen in other sectors.
00:04:03
Speaker
Or is it being seen or is it the expectation? I mean, there's, I think, very early signs of it being seen. um You know, you see a lot of these headlines about, ah you know, companies reducing the size of their workforce, but also getting a lot of guidance from companies, particularly in the healthcare care sector, about how AI is improving the efficiency of their research and development. So AI is making things more efficient and therefore, They don't have to spend as much, but still, you know, producing the same amount of and know drug development or whatever it is. This is, you know, incrementally positive for for earnings at this point. So back of the envelope math and just to size it up, because maybe that's, you know, an interesting way to frame it.
00:04:47
Speaker
We've been kind of estimating that this year alone, AI, in terms of the productivity improvement and the amount of costs it could save, could be in the region of $100 billion. dollars And that's for the US? Yeah, for the S&P 500. So it's huge numbers at this point. And again, a key driver of, ah you know, US companies and beating expectations. And we don't think that AI productivity aspect is something which is properly incorporated into consensus expectations at this point.
Forecast: AI and CapEx Surge
00:05:16
Speaker
Interesting. So this particular indicator, the Q1 earnings results seem to suggest that there is a reality in terms of the AI impact.
00:05:27
Speaker
So second statistic, $700 in capex for hyperscalers 2026 $850 billion, these are of course forecasts, capex for hyperscalers in 2027. and eight hundred and fifty billion dollars these are of course forecasts capex for hyperscaers in twenty twenty seven These are eye-watering numbers. Give us some context. Well, just to put it make it even more eye-watering and to put it into context, um you know, $850 billion dollars expected next year. Just a month and a half ago, the consensus was thinking it was $700 billion. dollars At the beginning of 2025, the consensus was expecting it to be just $300 billion. dollars So the amount of CapEx investment is absolutely huge. And this CapEx, which these hyperscalers and the Mag7 are using to build out data centers so that they can train their AI models, is having just such a big impact on, you know, multiple things.
00:06:23
Speaker
And these data centers, this is global spending on data centers, not just in the US, is it? So the 700, 800 billion dollars of ah CapEx is from the Magnificent Seven or the hyperscaler specifically.
00:06:38
Speaker
That money is spent in in a different ways. Data centers, not just in the US, but also outside of the US, but majority of it is spent for data centers inside. But the very interesting component here is that of that data center spend, 60% is essentially on ah memory and GPUs and semiconductors. And that is being spent all over the world. um And so that has a big impact on emerging markets like Korea and Taiwan.
AI-Driven Economic Growth
00:07:11
Speaker
But of course, you know, one of the interesting narratives about this capex spend is because it is so significant, one of the big questions has become is like, is this sustainable? And can companies really generate a profit off all of this capex spend? And that has become the biggest question, I think, from investors when it comes around AI at this point.
00:07:32
Speaker
And are issues also like where do you build them? They're starting to be pushback, electricity supply. These are factors that are in the news constantly in the US now, for example.
00:07:43
Speaker
Yeah, and there's also been reports that have said, you know, despite all of this expected spend and the site, you know, the hyperscalers being very committed to all of this, you know, there's actually signs of some of the the data centers specifically having quite significant delays behind schedule because ah shortage of construction workers, for example, that that you need. But nonetheless, it is having a huge impact on economic growth. You know, HSBC's, you know, US s economist Ryan Wang is basically estimating it's adding almost a percentage point to US s growth, which is huge to think about it in in the context of things. But you know what is, i guess, you know one of the takeaways from the US earnings season is that so far the monetization aspect is not really a problem. OpenAI, i Anthropic, these key you know AI model builders are basically generating
00:08:35
Speaker
ah revenue, at least at an annualized basis of close to $30 billion dollars each respectively, from companies that basically were generating zero dollars in 2023. It's just exponential growth. And so at least for the time being, you know, what the MAG7 is basically showing us is that AI is being adopted massively all over the world.
00:08:55
Speaker
They are clearly monetizing it. Their earnings are going through the roof. And the likelihood is, is that those capex estimates are going to be revised higher not lower over the next few months. And just one more piece of context, Alistair, this is obviously a lot in an absolute number, but is there any sector that has had this scale of CapEx expenditure in such a short period of time? I mean, I think this this level of CapEx is somewhat unprecedented. I mean, there's definitely, i guess, comparisons back to the 2000 build out around the internet or even going into the sort of you know, in the industrial revolution with the the amount of capex that were needed for um you know building railroads, ah etc.
00:09:40
Speaker
you know I do think the the difference this time round is a the speed of adoption of AI is much quicker than it you know it was during the internet and also, of course, railroads, which took decades to kind of build. As illustrated by the fact that these numbers are jumping so quickly. Exactly. And so the the speed and intensity is, um I think,
Challenges: AI Growth and Supply Bottlenecks
00:10:04
Speaker
unprecedented. And that is creating bottlenecks across the system. um You know, like I said, the fact is, that 60% of this capex spend um is being spent on things like semiconductors and memory and and DRAM. And, you know, there is a huge supply deficit of this at this point.
00:10:25
Speaker
So this brings us to our third eye-watering statistic. 400%. This is how much Korea semiconductor stocks are up year on year. What does this say about the impact of AI, this tech spend on emerging markets and stock markets outside of the US?
00:10:44
Speaker
So I think it shows you how EM has surprisingly been positioned as one of the key beneficiaries from this AI narrative. And all of this capex that the hyperscalers are you know generating, a huge amount of it is actually being directed towards areas like Korea and Taiwan. Korea specifically, they produce a type of semiconductor called DRAM. We estimate that roughly 20% of all hyperscaler capex is basically being spent on DRAM specifically. And so you've just seen this huge rally in the semiconductor names on on the back of it. And again, the revenue expectations for these kind of Korean ah semiconductor stocks has gone from them generating roughly $100 billion dollars of revenue um in 2025 to almost $400 billion dollars by 2027, 2028. Now, a few kind of interesting aspects here, which is, first of all, when you look at their valuations a price-to-earnings basis, actually, these stocks are some of the cheapest globally. They trade on around five times earnings. And one of the reasons for that is because I guess the market is unsure about whether this capex spend is sustainable and therefore whether the revenues from these career memory names is sustainable as well.
00:12:05
Speaker
And that is a slightly different perception than what you're finding in the US market. I would argue, yeah, where valuations are ah significantly higher. And because the Korean memory stocks and the Taiwan semiconductor stocks have rallied so aggressively, of course, investors are asking the question whether
Emerging Markets and AI Opportunities
00:12:22
Speaker
it is a bubble. And one of the things i would kind of allude to you is that there's actually a lot of analogies to the EM equity market rally back in the early two thousand um In the early 2000s, the EM market was one of the biggest beneficiaries from what we call the commodity supercycle. Actually, there's a very similar analogy to today, which is emerging markets are benefiting from a commodity supercycle.
00:12:46
Speaker
This commodity supercycle, though, is semiconductors and DRAM. And you can consider DRAM and memory and semis as a commodity. It is just a commodity in the AI era.
00:12:58
Speaker
And so in some ways, what we're seeing is almost a repeat of this super cycle for emerging markets in the early 2000s. But there's also difficulties that come with this. One of the difficulties for investors is that the EM equity market performance has become very narrow.
00:13:15
Speaker
So only 25% of stocks have actually been able to outperform the broader EM index because there's basically the EM market is being driven predominantly by this one very specific theme. It's very concentrated. It's very concentrated. And actually the concentration is at record highs.
00:13:31
Speaker
Which is actually, again, a difference between what you've described in the US market. Yeah. So I do think, you know, this AI narrative is is benefiting EM a lot as an asset class, but actually it's not benefiting everyone equally. It is benefiting only a few very specific markets and stocks at this point. And I think you've done quite a lot of research looking at AI in emerging markets and adoption and and sectors impacted.
00:14:00
Speaker
Yeah. and And I think that to me is the the next leg of the story, which is, well, how can this AI narrative broaden out to benefit the rest of emerging markets? You know, one of the things that we've often argued is that emerging market companies have historically been slightly less productive than the US and that AI can be a tool for them to leapfrog to a certain extent, improve their productivity. you know There's a lot of low hanging fruit. And again, we're actually, and then this to us was a very surprising thing, actually very seeing very clear, tangible examples of companies in EM using AI to improve profit margins, et cetera. And
00:14:41
Speaker
perhaps again to compare emerging markets, developed markets, one of the additional benefits that EM has is that the potential trade-offs and the downside risks around ai displacing employment and causing you know higher unemployment is actually less of a risk for emerging markets than it is for developed markets because AI, you know, what kind of jobs do they take? It is generally those that are in the services are generally paid more. And actually, EM has less exposure to that than developed markets do. So you could say emerging markets are in the sweet spot of the AI productivity here, which is that, you know, the companies that are listed can use it and they can improve their productivity. But the impact to the overall economy in terms of the negative impacts actually might be less than what we see in
Conclusion and Listener Engagement
00:15:27
Speaker
developed markets. Fascinating. Alistair, thank you so much. And let's speak again soon to see how the story develops.
00:15:34
Speaker
Thank you.
00:15:41
Speaker
Alistair Pinder there on how artificial intelligence is driving stock markets higher. A reminder that you can keep up to date on all of our latest reports, videos and podcasts, including this one, by downloading our app from Apple's App Store or Google Play.
00:15:57
Speaker
And while you're there, check out our sister podcast, Under the Banyan Tree, which focuses on Asia economics and markets. Finally, you can get in touch with us at askresearch at hsbc.com if you've got any comments or questions.
00:16:14
Speaker
That's it for this week's episode of The Macro Brief, hosted by me, Aline Van Dyne in New York and produced by Tom Barton in London. If you're new here, then please like and subscribe wherever you get your podcasts.
00:16:28
Speaker
And we hope you'll join us again next week. So until then, thank you very much for listening.