Ep 21: Investing in AI

🐰 Curious about how to invest in AI? Let's fall into the Artificial Intelligence rabbit hole, shall we? And, yes we talk about actual AI stocks as well. Beware the Jabberwocky (aka AI Tools that claim to "beat the stock market" or predict the best stocks to buy).

🤖 AI stocks are beating earnings upwards of 40% (and much much higher). Are analysts not valuating these stocks properly? Perhaps we all need a little lesson on how AI works and the earnings potential across all sectors in terms of these companies that are part of the AI frenzy.

Episode Equity

The AI revolution is here. Revolutions are transformative.

That’s right, the AI revolution isn’t coming, it’s already here, and it’s transforming everything faster than you can say 'Microsoft’s ChatGPT or Google’s Bard.' If you’ve been using these tools, you’ve likely seen your productivity soar faster than Powell’s rate hikes – or at least like your caffeine levels on a Monday morning.

Now, let’s talk revolutions and investments, shall we? Oh, how we all kick ourselves for not snapping up those Apple shares when they were just a tiny seedling! But what if we had a time machine, or better yet, applied what we have learned from history to pinpoint innovation? I’m here to give you a metaphorical pair of AI-powered binoculars to spot those investment opportunities.

Yes, the dotcom era and AI both share the shimmer of revolution, but not the same characteristics that burst the dotcom bubble. This isn’t about frothy speculation; it's about substantive, seismic shifts. So, while the naysayers are busy warning about bubbles, the savvy among us are using AI to spot the next wave of opportunities that are as ripe and real as the fruit in those early tech orchards. In the realm of investment, it's crucial to sift through the hype and look for companies that are not just promising but also pivotal. Key questions should guide us: Is the company solving a significant problem, a macro headwind? How will this translate into revenue? What does the cost structure look like? Is there a team with solid management skills at the helm? And crucially, is there a clear path to profitability?

The focus SHOULD be on productivity and efficiencies rather than speculative excess. Here’s why:

The AI Revolution has arrived, and it brings undeniable transformative power. While history appears to be repeating itself, it's not the bubble phenomenon that accompanies this revolution, but rather the enhanced productivity and efficiencies it offers.

It is evident that AI is revolutionary, just like the internet itself. However, unlike the dotcom bubble that occurred alongside the internet revolution, the upcoming data revolution driven by AI will not lead to a similar bubble. Instead, it will contribute to increased productivity and help address a crucial problem: the imbalance in the labor market.

  • The AI hype is largely supported by a small amount of mega-cap established companies, whereas the dotcom bubble was driven by a large number of young companies. (young companies without a clear path to profitability)
  • The hype is real as recent earnings are indicative of profitability and a clear path of expanding profit margins. The chips are in demand!
  • Post dotcom bubble productivity numbers increased. Allowed for increased output for less hours worked. Adoption is accelerated in comparison to the world wide web. 64% of business expect AI to increase productivity according to a Forbes Advisory Survey

What actually causes a bubble?

Basics. When sellers drastically overpower buyers or in other words, demand diminishes, and supply overpowers. This concept was exasperated during the dotcom bubble. Optimism outweighed pessimism. When there is a huge influx of selling it sends stock prices down. During the dotcom boom, there were a ton of IPOs which come with lock out periods, (meaning those who participated in the IPO were not permitted to sell for a certain period of time). These lock outs all expired around the same time… causing more selling pressure. Boom. The bubble burst.

DotCom Bubble Comparisons to AI

There are indeed similarities but also important differences. AI companies are indeed highly valued based on their potential, similar to the dot-com companies. However, many AI companies have more tangible assets and clearer paths to profitability. They are also operating in a market that is more mature and has a better understanding of the value and implications of new technology. And hey, the world wide web gave us access to data, you want an answer, you can google it.

From Data Revolution to Data Curation: Following the data revolution, productivity fast-tracked. Productivity has plateaued but will accelerate quicker than Powell raised rates. It already is beginning. Watch the productivity numbers.  


We all know that the world wide web is used in every single vertical. Whether you work in healthcare, technology, finance or in the retail sector… you are using the internet. This wasn’t always the case, nor was this expected during the dotcom boom. Well, that was a mistake.

The AI frenzy will increase productivity, a solution we desperately need.

Many companies are getting shareholder pressure to increase operating efficiency – AI and automation are an active part of this discussion. The spending is there even with the hype.

  • Opportunities in cyber security, software libraries, and TPUs.
  • The companies who own the largest data sets are primed to benefit.

Which means productivity will be the catalyst for the next leg higher in the market.

How do you recognize the opportunity and invest in AI?

I joined Charles Payne, on Making Money and laid out how. (side note: I love working with Charles’ team)  


I divide the investment opportunity into 5 separate categories:

  1.   Companies that are required to build literal LLM ML/ Chat GPT like models. Chips, software, cloud, etc. – this is NVDA, INTC, GOOG, AMD, META. Watch for demand this tells us that the revolutionary aspects are being adopted.
  2.   Companies that solve headwinds that arise from the models. Cybersecurity, adaptation accelerators, training framework – this is CRWD
  3.   Companies that offer direct to consumer solutions. Adobe Firefly, Chat GPT, Bard, hopefully AAPL!
  4.   Companies that offer direct to business models. IBM, CRM
  5.  Companies that adopt these models to expand profit margins.This is the final stage, and the longest. If you have ever worked at a large corporation, implementing technology will take some time. I imagine we will see this in technology first, followed by healthcare and financial services.

Consider this: ChatGPT sprinted to 100 million users in a mere two months, leaving the growth rates of social media giants like TikTok, Instagram, and Twitter in the dust. This isn't just quick; it's record-setting pace. Yet, it's important to recognize that the latest AI models, despite their prowess, are still in their early stages—like a digital 'teenager,' full of potential but just starting to figure out their place in the world. The applications springing from these models are only beginning to scratch the surface of what's possible.

AI's integration into business processes has been ongoing for decades, subtly fine-tuning the gears of the corporate machine. However, the performance leap in today’s AI models is something else entirely. It's not just a step but a giant leap in corporate efficiency and productivity. What we’re seeing is the prelude to a potential upheaval, a disruption that could ripple through every sector in the next 5 to 10 years. It's a corporate evolution, powered by AI, that could redefine the very fabric of how businesses operate and compete.

Remember investing involves risk, higher profit potential often equates to higher risk.

Shout out to Rachel Woods!

I learned a lot from her about how AI works (follow ⁠@the.rachel.woods⁠ on TikTok to learn a ton about AI), what's needed to create a large language model, GPUs/TPUs, and who already has alllll the data.

You should also checkout her website for AMAZING AI resources: ⁠https://theaiexchange.com/⁠

Jessie's Questions

Episode Transcript