The market plausibly expects AI software to create trillions of dollars of value by 2027

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).

1. 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). 
2. 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.
3. 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).
4. 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.
5. 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?
6. 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.

You cannot derive revenue, or the shape of revenue growth, from a stock price. I think what you mean is consensus forecasts that support the current share price. The title of the article is provably incorrect.

I agree risk also comes into it – it's not a risk-neutral expected value (I discuss that in the final section of the OP).

Interesting suggestion that the Big 5 are riskier than Nvidia. I think that's not how the market sees it – the big 5 have lower price & earnings volatility and lower beta. Historically chips have been very cyclical. The market also seems to think there's a significant chance Nvidia loses market share to TPUs or AMD. I think the main reason Nvidia has a higher PE ratio is due its earnings growth.

As models are pushed into every computer-mediated online interaction, training costs will likely be dwarfed by inference costs. NVidia's market cap may therefore be misleading in terms of the potential magnitude of investment in inference infrastructure, as NVidia is not as well positioned for inference as it is currently for training. Furthermore, cloud-based AI inference requires low-latency network data centre (DC) access. Such availability will likely be severely curtailed by the electrical power density that is physically available for the scaling of AI inference. i.e. cheap electricity for AI compute is near nuclear and hydro power, and typically not near major conurbations, and suitable for AI training, but not for inference.

How would you factor in exponential growth specifically in AI inference? Do you think this will occur within the DC or in edge computing?

I suspect AI inference will be pushed to migrate to smartphones due to both latency requirements and significant data privacy concerns. If this migration is inevitable, it will likely drive a huge amount of innovation in low-power ASIC neural compute.