How To Invest In AI Stocks

Wednesday, December 30, 2020, 6:00 PM | Leave Comment

Artificial Intelligence (AI) stocks are all the rage. But if you’re not careful, you’ll carelessly invest in companies that merely attach “AI” to their description.

Adding “AI” to a company’s name doesn’t make it an AI company. It’s like when companies added “dot-com” to their descriptions during the 1990’s bubble.

Luckily there is a way to invest in AI companies in a disciplined manner.

This article will equip you with the tools and framework necessary to analyze any AI investment idea.

Here’s what you’ll learn:

• What makes a great AI company

• What matters when analyzing AI stocks

• How to filter and decide which AI company to invest in

Let’s dive in!

What Makes A Great AI Company

A great AI company takes internally generated data and uses it to provide a better service/product to its users. Which in turn grows its user base and creates more (and better) data. Which begets a better product. Etcetera.

Great AI companies are true beneficiaries of the first-mover advantage. At the end of the day, whoever has the best data wins. The best data trains the best model which produces the best product.

But data for data’s sake isn’t enough. Great AI companies need the right type of data. Defensible data. In the case of AI, it’s the data that creates the economic moat, not the technology. In fact, Vorhies argues that the actual technology aspect of AI companies is purely a commodity.

So, we can reduce the formula for a great AI company into two factors:

1. The company that has a monopoly on the right type of data

2. The data is used to solve a specific (niche) problem

Armed with this framework, we scan areas where AI can create both a differentiated data set and solve a unique problem. This process will eliminate most industries from investment.

This leads us to an important (albeit nuanced) conclusion: data network effects are better signals for potential moats than regular network effects.

The truly great AI companies leverage both sets of network effects to create the best product and service for its customers. They do this by solving unique problems via insights from differentiated datasets. Defensible datasets. Welcome to the new economic moat reality.

Summary: You want to invest in AI companies that meet the following criteria …

• Defensible datasets

• Data used to solve real world problems

• First-mover advantage in a (preferably) niche market

• Industries ready to accept AI intervention and application

How To Find Great AI Companies

There’s no “quick-and-dirty” way to find great AI companies. You have to dig. And you have to do more work than other investors if you really want to find great (and undiscovered) ideas. That said, there’s a few things I do to jumpstart my AI research.

First, I do a quick Google Search for anything “AI Investment” related. Usually there’s whitepapers by large investment banks highlighting droves of AI-related investments.

After I have a list of ~3-5 research papers on the AI industry, I make a watchlist of names that are mentioned in those papers.

Think of that as the “top-level” funnel.

Summary: Google Search “AI Investing” Research Papers from Big Name Investment Banks

The next thing I do is find an AI-related ETF. Cautionary note here, many “AI” ETFs are really just technology ETFs with the usual suspects (NVDA, FB, GOOGL). That’s not to say those aren’t good ideas (they are). But the point of finding great AI companies is to find dedicated AI companies. Companies whose sole mission is centered on AI.

I’ll find a few (2-4) AI ETFs and scour through their individual holdings one-by-one. This extends my list from the initial Research Paper step above. By this point I have between 20-40 potential ideas to choose from.

Summary: Find AI-specific ETFs and go through each individual holding one-by-one.

Once I have this meta-list of potential AI investments, I proceed to the final step in the AI investment analysis process: Filtration

How To Filter Potential AI Investments

The last step in our AI analysis process is to filter our large list into a few names that get us really excited. This filtration process is both quantitative, qualitative and technicals based.

Here’s what we consider in each part of the process:

Quantitative: Total Market, Unit Economics & Cash Flow

This means we focus on unit economics. We go up the income statement. We want to see:

1. High revenue growth

2. High customer growth

3. Expanding (or high) gross margins

If each of those three metrics are up and to the right, we keep the name on the list.

Qualitative: Does It Solve A Problem?

Once it passes the quantitative screen, the company must answer “Yes” to each of these questions:

• Does this product solve customers’ problems?

• How much value does the product create for the customer (dollar savings or revenue generated)?

• Would the customer be worse-off if this product didn’t exist?

Technical: Is The Chart Bullish? Bearish? Or Neutral?

The last piece involves technicals of the chart. We want to buy AI stocks that are breaking out into new highs or breaking out from long-term consolidation. This means institutions/insiders/hedge funds are buying the stock and we can join the wave.

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