Summary
What it does: enterprise software.
Elevator pitch: arguably one of the finest franchises ever built, with deep distribution advantages that let it deliver innovations cheaply to a huge customer base.
Mental model: moat (read about my mental models here).
Valuation and potential returns: 21x June 2027 EPS estimates and growing EPS 20% per year.
Exchange and ticker: Nasdaq; MSFT.
Stock price and market cap: $421, $3.1tn.
Do I own it? Yes
IR website: here.
Tag for finding my other articles on this stock: MSFT
Links to Part I: Silicon and Infrastructure and Part III: distribution, bundling, and combining layers.
About this blog: I have been investing for 25 years, professionally and personally. I look for stocks that have a high probability of compounding at 15% for at least 5 years with limited downside. I write these stocks up on my blog. You can find more about me, my philosophy, my mental models, and my portfolio structure on my site.
Disclaimer: This post is for informational and educational purposes only. Building Arks is not licensed or regulated to provide any financial advisory service and nothing published by Building Arks should be taken as a recommendation to buy or sell securities, relied upon as financial advice, or treated as individual investment advice designed to meet your personal financial needs. You are advised to discuss your personal investment needs and options with qualified financial advisers. Building Arks uses information sources believed to be reliable, but does not guarantee the accuracy of the information in this post. The opinions expressed in this post are those of the publisher and are subject to change without notice. The publisher may or may not hold positions in the securities discussed in this post and may purchase or sell such positions without notice.
Part 1 of this review introduced the layers of AI and dived into two, silicon and infrastructure. It concluded that
Microsoft is behind in silicon because it started late, but Maia 200 is promising and Microsoft will close the gap.
Infrastructure will commoditise slowly, and a new utility compute industry will eventually allow hyperscalers to go (partway) back to being capital light.
Part 2 dives into the middle two layers: LLMs and Agents.
LLMs
Large Language Models are the engines behind AI. Is this a winner-take-most market, or will LLMs be commoditised? If it’s winner-take-most Microsoft has a real problem. It doesn’t own a leading LLM, so it won’t be the winner; in order to sell AI to its customers, therefore, it will have to buy from the winner, who will have the whip hand in negotiations and might bypass Microsoft entirely to own the customer relationship directly.
Winner-take-most could come about in two ways:
One LLM lab reaches artificial general intelligence first, it could pull ahead of the others in performance terms. AGI isn’t well-defined, but in this context it means the LLM would improve itself faster and at lower cost than is possible with humans managing the process.
Several LLM labs run out of money, leaving only one.
I think it is vanishingly improbable that either of these things will happen, for a variety of reasons.
The first is that it is in literally nobody’s interests for one LLM to dominate the future. Here’s a short list of people who would absolutely hate this outcome: