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!
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!