Investing In The AI Revolution: Are You Ready?

Recently, one of the members of our FB group asked how to invest in Artificial Intelligence (AI)  and it got me thinking- with ChatGPT’s fast adoption and Google’s release of Bard, are we on the precipice of the AI revolution that technologists have long talked about?

The big tech companies that have invested the most in Ai stand to benefit. Microsoft, for instance, invested in Open AI, maker of the now-popular ChatGPT. Similarly, Google’s market cap increased by over 5% just a few days after Google’s annual demonstration conference, Google I/O. 

The stakes are high. And just like with smartphones, I’d expect larger companies to duke it out.

Why get left behind? Take some time to learn this shift and capture new opportunities with AI investing.

This post was submitted by Dr. Nirav H. Shah.

 

What Are The 3 Technologies That Make Up Artificial Intelligence You Can Invest In?

1. Chips
2. Software applications
3. Cloud, databases, and cloud computation

 

Here’s how you might think about AI companies – the chips, the cloud computing, and the front-end technologies. Some companies, like Apple, Tesla, and Google, focus on multiple of the above. 

Chips and Cloud: These are key foundational pieces to the AI revolution. The processors and cloud platforms that power these services are advancing rapidly. Companies like Nvidia, Intel, ARM Holdings, Google Cloud, Amazon Web Services (AWS), and Microsoft Azure all provide leading-edge technologies in this space.

 

Chips

 

This is where all the magic happens. To process the models at the hardware level the chips have had to improve radically. NVIDIA has been a huge beneficiary of this due to its Graphics Processing Unit (GPU) technology which happens to work well for AI  computation.

• Nvidia
• Asml
• TSMC
• Amd
• Intel

 

 

Software Applications

 

At this stage, we witness the real breakthrough of innovation. AI’s core, machine learning algorithms, have undergone significant advancements. These sophisticated algorithms enable machines to learn from data and accurately make decisions or predictions.

They power various applications ranging from recommendation engines to self-driving cars. Hence, AI’s rise has led to an explosion in the value of data, a critical asset in today’s digital economy. Cloud computing’s emergence has given AI the infrastructure it needs to operate on a large scale.

The cloud’s unlimited processing power and storage capabilities allowed AI to analyze massive data sets and complex models, which was prohibitively expensive just a few years ago. Hence, AI’s rise has led to an explosion in the value of data and algorithms, two critical assets in today’s digital economy.

Lastly, cloud computing’s emergence has given AI the infrastructure it needs to operate on a large scale. Leaders in cloud-based data would have a predictable advantage in this Ai landscape. 

• Microsoft – Outlook, Open AI
• Google –  search, BARD, Google 
• Tesla – self-driving cars
• Meta – recently released AI Sandbox
• Apple from Siri to FaceID, AR/VR anticipated
• Tencent – maker of WeChat and much more
• Baidu – search engine, translation

 

Cloud, Databases, and Cloud Computation

 

Powering this amount of computing in the cloud will take significant servers running at what has been dubbed ‘hyper-scale.’ These companies power the internet and mobile applications we have become used to. Everything from calling a car on an Uber/Lyft to ordering groceries via Amazon/Instacart could apply to this. The need for cloud computing is increasing so fast that Microsoft’s Azure and Google may have to partner for Open AI’s cloud needs.

• Google
• Amazon
• Microsoft
• Oracle

 

Public Companies and AI Exchange Traded Funds (ETFs)

 

Have you considered investing in AI ETFs? It’s another option to explore. I looked them up to see if any unique AI ETFs had the benefits someone might be looking for. 

Some of the most popular AI ETFs I came across there the following:

• ROBO Global Robotics and Automation Index ETF (ROBO)
• Global X Robotics and Artificial Intelligence ETF (AIQ)
• iShares Robotics and Artificial Intelligence ETF (IRBO)
• First Trust Nasdaq AI and Robotics ETF (NOAR)
• iShares Exponential Technologies ETF (IBUY)

 

In general, most of these so-called AI ETFs significantly emphasize robotics, which, while also interesting, is very different. Robotics technologies will cost much more to build, have a different margin profile than other sectors like software and chip-makers, and tend to be capital intensive and, therefore, potentially dilutive to investors. Some of these ETFs also included companies like Salesforce and Hubspot. Knowing people at both those companies, I would not call them AI-centric by any means, but they will undoubtedly benefit from AI like much of software development and applications will.

 

Private Companies

 

There are ways to invest in private companies, but typically that’s out of reach for most investors unless they’re accredited and have a connection to invest. These are high-risk and difficult to appraise as many private startups claim AI in anything they do. 

Some of the interesting private AI companies I’ve been following are:

• Scale.com – helps enterprises leverage data pipeline for AI/ML
• Standard Cognition – Autonomous retail for brick & mortar stores.
• Turing.com – finding engineers via the cloud matched by AI
• Rad.AI – radiology-based imaging reading guidance
• Cerebras – Next-gen chip with the largest processor for AI applications

If you’d like more about these, email us, and I’ll write more and send you some personalized recommendations.

 

The Potential and Conclusion

 

In summary, AI will be infused into many businesses, so trying to get a unique alpha for returns will be tricky unless you remain focused on a few areas. It’s also another potential hype cycle. NVIDIA has a price-to-earnings ratio of around 202 which is incredible compared to the broader S&P, which is around 23-24 recently. Hence, the stock is heading into a momentum stock and not based on fundamentals.

 

 

The best outcomes will be in businesses with a service component that AI can easily improve the margin profile of, for example, law firms and accounting. PWS announced a 1 Billion dollar effort to invest in AI technology for its firm. PWC, as a company, has an estimated margin of 20%. Suppose the revenue is $50.3 billion. That means the firm has $10Bn of potential profits. If the firm can improve its efficiency and margin by 5%, that would create another 2.5Bn of profits!

Investing in individual companies is tricky and may not provide the best results, but if you plan to pick a company, consider tracking ones that are discussing AI in their earnings releases and then seeing if the improvement is occurring with regard to the efficiency of the business. One way to track this is revenue per employee; another way to evaluate this is by improving margin. Many tech companies are overstaffed, so tracking a company’s progress in implementing AI seriously for operational efficiency will take quarters, if not years. 

For me, that’s the real potential of AI. It can be so deflationary and improve efficiency that fewer people can have more productivity and output. Perhaps the best way to invest in it is for us to use it in our pursuits and businesses.