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The appetite for buying gold has increased significantly in recent months in India, see chart below.
Note: Gold prices are INR per troy ounce. Sources: Association of Mutual Funds in India, Macrobond, Apollo Chief Economist See important disclaimers at the bottom of the page.
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The amount of money being spent on capex by hyperscalers is enormous at around $646 billion in 2026.
The charts in this chart book put that number into perspective.
Specifically:
- Hyperscaler capex is expected in 2026 to be at approximately $646 billion, or about 2% of US GDP.
- For comparison, annual growth in consumer spending is currently around $1 trillion.
- US hyperscaler capex at $646 billion is roughly equivalent to the size of GDP for Singapore, Sweden and Argentina.
- For comparison, total US bank loan growth in 2025 was around $700 billion.
- For comparison, defense spending in 2025 was at $917 billion.
- Hyperscaler capex is more than the combined military spending of Germany, France, UK, Japan, Italy and Canada.
- Hyperscaler capex is roughly the same size as the market cap of the stock markets in Belgium, Denmark and Indonesia.
Sources: FactSet, Apollo Chief Economist See important disclaimers at the bottom of the page.
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If the economy enters a recession, it is a problem for all credit markets. But there are also situations where only one sector is hit by some shock, while the rest of the economy is fine.
This is the situation we are in today with software.
For software, the problem is not only the threat from AI but also the rise in the cost of capital following the Fed’s 2022 interest rate hikes.
History is full of episodes where changes in the cost of capital and sector-specific factors have created opportunities in credit markets, see chart below. For example, the energy credit cycle from 2014 to 2016, the brick-and-mortar retail cycle from 2016 to 2019 and the commercial real estate cycle from 2022 to 2024.
The bottom line is that individual sectors of the economy can be in distress while the rest remain fine. In fact, that is what creative destruction and disruption are all about.
In short: In a capitalist economy where new products are invented every day, the only constant is change, and that is where opportunities arise for credit investors.
Source: Apollo Chief Economist See important disclaimers at the bottom of the page.
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The latest 30-year Treasury auction shows continued strong buying of US duration with public tendered demand at the highest level on record, see chart below. Indirect bidders, who are normally a proxy for foreign demand, took 69.8%, well above the historical average at 64%. Primary dealers, who have to buy what others don’t, were left with only 5.9%. The bottom line is that Treasury auction metrics show that there continues to be very solid demand for the long end in US Treasuries.
Sources: Bloomberg, Macrobond, Apollo Chief Economist See important disclaimers at the bottom of the page.
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The private sector in the US is in really good shape. Households have been deleveraging since the 2008 financial crisis, see the first chart. The corporate sector has been deleveraging since COVID, see the second chart.
Unfortunately, the government balance sheet is not in good shape. Government debt to GDP has increased steadily from 40% of GDP in 2007 to more than 100% today, see the third chart.
Sources: Federal Reserve, US Bureau of Economic Analysis (BEA), Macrobond, Apollo Chief Economist 
Sources: Federal Reserve, US Bureau of Economic Analysis (BEA), Macrobond, Apollo Chief Economist 
Sources: Federal Reserve, US Bureau of Economic Analysis (BEA), Macrobond, Apollo Chief Economist See important disclaimers at the bottom of the page.
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A unicorn is a privately owned company valued at more than $1 billion, and over the past decade, the number of unicorns has increased from 114 to 857, see chart below.
With a growing number of privately owned large companies, today there is a bigger need than ever before for private debt and equity solutions for both performing and distressed firms.
Note: The term unicorn describes startups that are privately held and valued at more than $1 billion. Sources: PitchBook, Apollo Chief Economist See important disclaimers at the bottom of the page.
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Looking at the entire global universe of public bonds outstanding shows that almost 90% trade at a yield lower than 5%, see chart below.
With inflation at close to 3%, this means that investors in public fixed income only get a 2% real return each year.
Sources: Bloomberg, Apollo Chief Economist See important disclaimers at the bottom of the page.
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Japan’s population growth has slowed and moved into sustained decline, as births continue to fall and the demographic gap widens.
Sources: Japanese Ministry of Health, Labour & Welfare; Macrobond; Apollo Chief Economist See important disclaimers at the bottom of the page.
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After the Global Financial Crisis (GFC), the P/E ratio for US equities was similar to that of the rest of the world, but the surge in tech valuations has now pushed the US P/E ratio 40% higher, see chart below.
Note: US=SPX Index, World ex-US=MSCI World ex-USA Index. Sources: Bloomberg, Macrobond, Apollo Chief Economist See important disclaimers at the bottom of the page.
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The Nobel Prize–winning economist Robert Solow said in 1987, “You can see the computer age everywhere but in the productivity statistics.” This observation is the so‑called Solow productivity paradox.
The same can be said today: AI is everywhere except in the incoming macroeconomic data.
Today, you don’t see AI in the employment data, productivity data or inflation data. Similarly, for the S&P 493, there are no signs of AI in profit margins or earnings expectations.
Maybe there is a J‑curve effect for AI, where it takes time for AI to show up in the macro data. Maybe not.
Whether there is a J-curve effect depends on the value creation from AI. There is fierce competition between the builders of large language models (LLMs), which is driving the price of LLMs toward zero for end-users. This is the opposite of what the finance textbook says, namely that an innovator will have monopoly pricing power until others arrive with a similar product. This is what we saw during the computer age in the 1980s.
In other words, from a macro perspective, the value creation is not the product, but how generative AI is used and implemented in different sectors in the economy. The academic literature is still inconclusive about the potential macro effects of AI, see table below.
After three years with ChatGPT and still no signs of AI in the incoming data, it looks like AI will likely be labor enhancing in some sectors rather than labor replacing in all sectors.
Source: Apollo Chief Economist See important disclaimers at the bottom of the page.
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