Thanks, Ben, for writing this up! I very much enjoyed reading your intuition.
I was a bit confused in a few places with your reasoning (but to be fair, I didn’t read your article super carefully).
Nvidia’s market price can be used to calculate its expected discounted profits over time, but it can’t tell us when those profits will take place. A high market cap can imply rapid short-term growth to US$180 billion of revenues by 2027 or a more prolonged period of slower growth to US$180B by 2030 or 2035. Discount rates are an additional degree of freedom. We can have a lower level of revenues (of not even US$180B) if we assume lower discount rates. CAPM isn’t that useful since it’s an empirical disaster, and there’s the well-known fact that high-growth companies can have lower, not higher, discount rates (i.e. the value/growth factor).
Analysts are forecasting very rapid growth for Nvidia’s revenues and profits. You mention Jan-2025 fiscal year-end revenues of $110 billion. The same source has analyst expectations for Jan-2026 year-end revenues of $138 billion. Perhaps you can find analyst expectations that go even further but these are generally rare and unreliable. So you could say that analysts expect Nvidia’s revenues of $138 billion in 2025 (ending Jan-26) and continue your analysis from there. However, analyst expectations are known to have an optimistic bias and aren’t as predictive as market prices.
I was confused about how you used the 3-year expected life of GPUs within your analysis. It’s irrelevant when it comes to interpreting Nvidia’s market price since Nvidia’s future sales pathway can’t be inferred by how long its products last. The more appropriate link applies to when Nvidia’s customers must have high sales levels given that Nvidia is selling its GPUs, say in 2025. If we add 3 (GPU life) to 2025 (last available year for analyst estimates), we get 2028 (not your 2027), with Nvidia’s revenues at $138 billion based on analyst expectations (not your US180 billion based on the market price).
I wasn’t sure why you needed to estimate ‘consumer value’ or ‘willingness to pay.’ This inflated your final numbers by 4x in your title of ‘trillions of dollars of value.’ And confusingly, it conflates how value is used in other parts of your article. Bringing in ‘consumer value’ is weird because it’s not commonly calculated or compared in economics or finance. Value generally refers to that implied by market transactions, and this applies to well-known concepts like GDP, income, addressable market size, market value, sales, profits, etc (how you use it in most of your article). So we don’t have a good intuition for what trillions of consumer surplus means, but, we do for hundreds of billions of sales.
So instead of ending with ‘trillions of consumer value’ for which there are no intuitive comparisons, it’s better to end with x billions of sales (profits aren’t reliable since high growth companies can go years and years without them, e.g. Amazon). You can then compare this with other historical episodes of industries/companies with high sales growth and see if this growth is likely/unlikely for AI. How fast did Internet companies, or the SaaS industry (software as a service), or Apple get to this level of sales? Is it likely (or not) that AI software companies can do the same within y years?
In case you haven’t seen these, here are some related resources that might be useful. 1) Damadoran’s valuation of Nvidia (from June 2023 so already dated given Nvidia’s rapid growth), 2) Sequioa’s talks on the large AI software potential (not much in terms of hard numbers but more for useful historic analogs), and 3) ARK’s AI note from 2023 (self-promoting and highly optimistic but provides estimates for the AI software market in the many trillions by 2030).
Thanks, Ben! I enjoyed reading your write-up and appreciate your thought experiment.
On the timing of the profits, my first estimate is for how far profits will need to eventually rise.
To estimate the year-by-year figures, I just assume revenues grow at the 5yr average rate of ~35% and check that’s roughly in line with analyst expectations. That’s a further extrapolation, but I found it helpful to get a sense of a specific plausible scenario.
(I also think that if Nvidia revenue looked to be under <20% p.a. the next few quarters, the stock would sell off, though that’s just a judgement call.)
On the discount rate, my initial estimate is for the increase in earnings for Nvidia relative to other companies (which allows us to roughly factor out the average market discount rate) and assuming that Nvidia is roughly as risky as other companies.
In the appendix I discuss how if Nvidia is riskier than other companies it could change the estimate. Using Nvidia’s beta as an estimate of the riskiness doesn’t seem to result in a big change to the bottom line.
I agree analyst expectations are a worse guide than market prices, which is why I tried to focus on market prices wherever possible.
The GPU lifespan figures come in when going from GPU spending to software revenues. (They’re not used for Nvidia’s valuation.)
If $100bn is spent on GPUs this year, then you can amortise that cost over the GPU’s lifespan.
A 4 year lifespan would mean data centre companies need to earn at least $25bn of revenues per year for the next 4 years to cover those capital costs. (And then more to pay for the other hardware and electricity they need, as well as profit.)
On consumer value, I was unsure whether to just focus on revenues or make this extra leap. The reason I was interested in it is I wanted to get a more intuitive sense of the scale of the economic value AI software would need to create, in terms that are closer to GDP, or % of work tasks automated, or consumer surplus.
Consumer value isn’t a standard term, but if you subtract the cost of the AI software from it, you get consumer surplus (max WTP—price). Arguably the consumer surplus increase will be equal to the GDP increase. However, I got different advice on how to calculate the GDP increase, so I left it at consumer value.
I agree looking at more historical case studies of new technologies being introduced would be interesting. Thanks for the links!
Thanks, Ben, for writing this up! I very much enjoyed reading your intuition.
I was a bit confused in a few places with your reasoning (but to be fair, I didn’t read your article super carefully).
Nvidia’s market price can be used to calculate its expected discounted profits over time, but it can’t tell us when those profits will take place. A high market cap can imply rapid short-term growth to US$180 billion of revenues by 2027 or a more prolonged period of slower growth to US$180B by 2030 or 2035. Discount rates are an additional degree of freedom. We can have a lower level of revenues (of not even US$180B) if we assume lower discount rates. CAPM isn’t that useful since it’s an empirical disaster, and there’s the well-known fact that high-growth companies can have lower, not higher, discount rates (i.e. the value/growth factor).
Analysts are forecasting very rapid growth for Nvidia’s revenues and profits. You mention Jan-2025 fiscal year-end revenues of $110 billion. The same source has analyst expectations for Jan-2026 year-end revenues of $138 billion. Perhaps you can find analyst expectations that go even further but these are generally rare and unreliable. So you could say that analysts expect Nvidia’s revenues of $138 billion in 2025 (ending Jan-26) and continue your analysis from there. However, analyst expectations are known to have an optimistic bias and aren’t as predictive as market prices.
I was confused about how you used the 3-year expected life of GPUs within your analysis. It’s irrelevant when it comes to interpreting Nvidia’s market price since Nvidia’s future sales pathway can’t be inferred by how long its products last. The more appropriate link applies to when Nvidia’s customers must have high sales levels given that Nvidia is selling its GPUs, say in 2025. If we add 3 (GPU life) to 2025 (last available year for analyst estimates), we get 2028 (not your 2027), with Nvidia’s revenues at $138 billion based on analyst expectations (not your US180 billion based on the market price).
I wasn’t sure why you needed to estimate ‘consumer value’ or ‘willingness to pay.’ This inflated your final numbers by 4x in your title of ‘trillions of dollars of value.’ And confusingly, it conflates how value is used in other parts of your article. Bringing in ‘consumer value’ is weird because it’s not commonly calculated or compared in economics or finance. Value generally refers to that implied by market transactions, and this applies to well-known concepts like GDP, income, addressable market size, market value, sales, profits, etc (how you use it in most of your article). So we don’t have a good intuition for what trillions of consumer surplus means, but, we do for hundreds of billions of sales.
So instead of ending with ‘trillions of consumer value’ for which there are no intuitive comparisons, it’s better to end with x billions of sales (profits aren’t reliable since high growth companies can go years and years without them, e.g. Amazon). You can then compare this with other historical episodes of industries/companies with high sales growth and see if this growth is likely/unlikely for AI. How fast did Internet companies, or the SaaS industry (software as a service), or Apple get to this level of sales? Is it likely (or not) that AI software companies can do the same within y years?
In case you haven’t seen these, here are some related resources that might be useful. 1) Damadoran’s valuation of Nvidia (from June 2023 so already dated given Nvidia’s rapid growth), 2) Sequioa’s talks on the large AI software potential (not much in terms of hard numbers but more for useful historic analogs), and 3) ARK’s AI note from 2023 (self-promoting and highly optimistic but provides estimates for the AI software market in the many trillions by 2030).
Thanks, Ben! I enjoyed reading your write-up and appreciate your thought experiment.
Hi Wayne,
Those are good comments!
On the timing of the profits, my first estimate is for how far profits will need to eventually rise.
To estimate the year-by-year figures, I just assume revenues grow at the 5yr average rate of ~35% and check that’s roughly in line with analyst expectations. That’s a further extrapolation, but I found it helpful to get a sense of a specific plausible scenario.
(I also think that if Nvidia revenue looked to be under <20% p.a. the next few quarters, the stock would sell off, though that’s just a judgement call.)
On the discount rate, my initial estimate is for the increase in earnings for Nvidia relative to other companies (which allows us to roughly factor out the average market discount rate) and assuming that Nvidia is roughly as risky as other companies.
In the appendix I discuss how if Nvidia is riskier than other companies it could change the estimate. Using Nvidia’s beta as an estimate of the riskiness doesn’t seem to result in a big change to the bottom line.
I agree analyst expectations are a worse guide than market prices, which is why I tried to focus on market prices wherever possible.
The GPU lifespan figures come in when going from GPU spending to software revenues. (They’re not used for Nvidia’s valuation.)
If $100bn is spent on GPUs this year, then you can amortise that cost over the GPU’s lifespan.
A 4 year lifespan would mean data centre companies need to earn at least $25bn of revenues per year for the next 4 years to cover those capital costs. (And then more to pay for the other hardware and electricity they need, as well as profit.)
On consumer value, I was unsure whether to just focus on revenues or make this extra leap. The reason I was interested in it is I wanted to get a more intuitive sense of the scale of the economic value AI software would need to create, in terms that are closer to GDP, or % of work tasks automated, or consumer surplus.
Consumer value isn’t a standard term, but if you subtract the cost of the AI software from it, you get consumer surplus (max WTP—price). Arguably the consumer surplus increase will be equal to the GDP increase. However, I got different advice on how to calculate the GDP increase, so I left it at consumer value.
I agree looking at more historical case studies of new technologies being introduced would be interesting. Thanks for the links!