🤖 My Favorite AI Stocks Right Now
Infrastructure and Efficiency...
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This post was actually inspired by a few people replying to other posts shared — so keep up the ideas! Happy to dive into anything you all deem important.
In case you missed my last “My Favorite Stocks Right Now” post, I’ve shared it below. The theme of that post was “cash flow,” specifically operating & free cash flow.
However, the theme of this post is going to be artificial intelligence.
We’ve talked about AI here and there — specifically as it relates to Perion Networks (PERI), Microsoft (MSFT), and Nvidia (NVDA) — but we’ve yet to share a condensed list of our favorite AI stocks.
I want to be clear — AI is a relatively “new” technology and there certainly are companies who (like Nvidia) have already seen immense run-ups in price, likely already “pricing in” their potential future earnings.
I don’t want this post to act as a “buy this now and make a ton of money,” but instead as inspiration for you to do more research and come to an investment conclusion yourself.
With that being said, in this post I’m going to:
Share the names of companies laying the infrastructure for AI
Share the names of companies who will benefit from the technology
When I think of AI companies I tend to believe there are two buckets — companies who are building the infrastructure, like Nvidia, and companies who are leveraging AI to become more efficient and generate more profits for their shareholders.
This post will be covering both of these subsets.
⚡️ Subset 1 — Infrastructure
I’m not going to dive deep into “what is artificial intelligence” and “why it needs infrastructure,” as I’m sure you could easily imagine the gigantic amount of computing power that’s needed to generate and run the countless AI use cases that have been built thus far.
Think about it like this — ChatGPT, the fastest adopted application of all time, was trained by using 10,000 graphics processing units (GPUs) clustered together inside of a supercomputer belonging to Microsoft.
GPUs accelerate neural network processing — a very important aspect to AI — exponentially when compared to regular processing chips.
What if you’re the company making the GPUs? And you sell them for $10K each (Nvidia)? That’s $100M in revenue for your business for supplying the needs of one supercomputer.
Now what if everyone simultaneously realized they need to begin building and training their own AI products?
That’s essentially why Nvidia’s stock price skyrocketed after they released their Q1 earnings report.
Management shared with investors that they’re now expecting to generate +$4B more in revenue during Q2 this year than originally anticipated because everyone all of a sudden wanted to buy their GPUs to train their own AI models on.
I know the below quote it long, but it’s incredibly important for you to read and understand — everyone wants to build with AI now.
“First, cloud service providers (CSPs) around the world are racing to deploy our flagship Hopper and Ampere architecture GPUs to meet the surge in interest from both enterprise and consumer AI applications for training and inference.
Multiple CSPs announced the availability of H100 on their platforms, including private previews at Microsoft Azure, Google Cloud, and Oracle Cloud Infrastructure, upcoming offerings at AWS, and general availability at emerging GPU specialized cloud providers like CoreWeave and Lambda. In addition to enterprise AI adoption, these CSPs are serving strong demand for H100 from Generative AI pioneers.
Second, consumer Internet companies are also at the forefront of adopting Generative AI and deep learning-based recommendation systems, driving strong growth. For example, Meta has now deployed it's H100 powered Grand Teton AI supercomputer for its AI production and research teams.
Third, enterprise demand for AI and accelerated computing is strong. We are seeing momentum in verticals such as automotive, financial services, healthcare, and telecom, where AI and accelerated computing are quickly becoming integral to customers' innovation roadmaps and competitive positioning.
For example, Bloomberg announced it has a 50 billion parameter model, BloombergGPT, to help with financial natural language processing tasks such as sentiment analysis, named entity recognition, news classification, and question-answering.” — Colette Kress, CFO of Nvidia
Okay, so who else is doing this?
Before I answer that question, I want to be very clear that I don’t believe Nvidia today is now a “I missed the boat” investment.
Considering just how disruptive AI will be, Nvidia can absolutely 3-5X in market capitalization over the coming 10-15 years (10-13% annual CAGR). I’m happily dollar cost averaging into Nvidia inside of my retirement accounts.
As I share the four “infrastructure” names below, I don’t want you to think in 3-6 month time horizons — but instead in 3-6 year time horizons. This “artificial intelligence stuff” is going to be around for decades.
With that being said, who hasn’t yet seen a crazy run-up in stock price?
👉 Broadcom (AVGO)
Broadcom is a semiconductor company. Broadcom provides specialized hardware components, such as processors and chips, optimized for AI applications. These components deliver the performance, energy efficiency, and processing capabilities required for AI workloads.
Broadcom’s bread and butter is energy efficiency, specifically through their application-specific integrated circuits (ASICs).
During their recent earnings call, their management team shared with us that AI-specific revenue is expected to make up 15% of their total semiconductor revenue — up from just 10% last year.
The company also now expects AI-specific revenue to make up 25% of their total semiconductor revenue in 2024 — certainly a good thing. I believe this trend will continue well throughout the rest of the decade, causing their free cash flow (and dividend) to climb higher.
The above chart shows the relationship between their stock price (black) and their free cash flow (blue) — it’s pretty obvious where things are headed in the coming years.