How many percent return?

The AI BOOM has two key words. ChatGPT and NVDA. Retail can directly invest in ChatGPT (a complex “non-profit except for Microsoft” structure); the following will be about the green stock $NVDIA.

The history of AI.

So, when did this concept of AI start? This post will choose the Theseus invention by Claude Shannon in the 1940s (opinions about the beginning may differ). 

A “special” maze was built. In this maze, a “mouse” could learn the way out in two passes. In the first pass, the mouse “studied” the terrain. In the second pass, it went straight from start to end. There is nothing magical when explanation hits the observer, just pure cleverness. The mouse tested all four possibilities in each case and closed the electric circuit when it hit a wall. When it finally arrived at the end of the maze, what lay behind was a series of “opened” electric circuits, where the mouse could circulate easily for the second pass. What is likeable about this starting point is that AI nowadays kind of does the same brute force learning, trying to find “the open” circuit by guessing and comparing. It is to be noted that all that was before computers. 

From there until the early 1990s, few progresses were made. This period ended up being called the AI winter. In 1998, the first chatbot was made by a British scientist.  From there, rapid progression was made, with breakthroughs, notably in image detection, until the famous ChatGPT 3.5 of 2022. 

Seen as a multi-faceted revolution, this post will opt for a less optimistic tone. The real revolution is to have shown directly how “strong” algorithms are to the common folks. Before that, AI was already present everywhere, in phones, computers, internets, production chains, etc. 

There are four factors determining the capacity of an algorithm: architecture, training data, reinforcement, and computation power. 

ChatGPT 3.5 scraped the whole web, getting a huge data set. Substantial reinforcement was done with people working around the clock to perfect the answers. Computation power was heavily sponsored by Microsoft. With all that, you end up with a powerful tool that has never been seen before, but architecture isn’t the cause of it. That becomes a problem if you want to get better tools, because data sets have a limit, reinforcement, and computational power too. It also has a cost (see further in the post). 

The goal transforms into doing better with less. That’s where Nvidia comes into play. 

What does Nvidia do?

Nvidia was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem, with the light motive of bringing 3D graphics to gaming and multimedia. Started by designing Graphics Processing Units (GPUs), which allowed them to gain recognition in the gaming industry. Recognition grew, making Nvidia one of the biggest players in the graphics card market. Finally, it expanded beyond gaming, with high-performance computing and artificial intelligence. In summary, Nvidia became the provider of jeans and shovels during the AI gold rush. 

A big chunk of its earnings comes from data centers (second place goes to gaming). Their powerful GPUs are used to accelerate the training of artificial intelligence and data science computation. 

One important thing to note is that Nvidia beat 19 out of the last 20 quarters' earnings expectations. This led to a sharp uprise in price, notably at the last earnings release. 

Market share.

After having heard “AI” almost everywhere, what are the ways to invest in it? There are two main choices. Nvidia, or one of the Magnificent Seven (who pounded the term AI intensely in their last earnings reports). Let’s look at the two most important market share parts, Data Center GPUs and Models & Platforms. 

For Data Center GPUs, Nvidia is the biggest vendor with 92% market share. AMD has 3% and Others 5%. It is also easy for anyone to invest in this sector, from retail to institutional. 

Above, with Models & Platforms, it becomes harder. OpenAI, which is the leading vendor, is a non-profit organization, but a sponsored one (by Microsoft), which retains shares in a sub-company aimed at bringing funds for development and computation (a complicated structure). Second is Microsoft. One could say that, in fact, since Microsoft indirectly owns OpenAI, Microsoft is the leader of the market share of models and platforms (39+30=77%). The rest of the market share is Amazon (8%), Google (7%), and others (16%). The difference between Nvidia and Microsoft is that Microsoft rhymes less with AI than Nvidia, since AI is only a small part of Microsoft's business. 

Roadblocks of AI. 

There are few roadblocks incoming. As said above, there are four factors that create a useful algorithm: architecture, training data set, reinforcement, and computation. 

Architecture is not subject to limitation, except the one of trial and error that can become costly. It all comes down to research. One of the problems is that algorithms become ultra-performant in one task, perfecting architecture for this task only. To be precise, it needs training, so every time, there is a trial-and-error cost. However, this is only a small roadblock compared to the next one.

A second roadblock concerns the training data. All the leading generative AIs (LLMs and other generative AIs) need loads of data to “fake” realness. Where does this data come from? Scraping the web. There is a major problem: plagiarism. Since these AIs scrape information from websites that didn’t necessarily give their authorization (either news, artists, etc.), this could lead to lawsuits and regulations. If regulations diminish the data set available, it will have an impact on performance. 

The third roadblock concerns reinforcement. This is less of a problem than training data. However, it can be one in terms of cost. These algorithms need to be “educated,” so real people are used to perfecting the answers they give. That step gives the “almost real” touch. The more you reinforce an algorithm, the more you need to hire people to do so (as OpenAI did for ChatGPT 3.5). There is also a constant reinforcement made when people are using the application, but this has less impact on the overall speech than the hammered-down reinforcement made before release. 

Finally, the fourth concerns cost. All this computing has a cost, and that’s why a part of OpenAI became privately owned by Microsoft (owns 49%). The problem is that the overall cost is growing. Algorithms become better, but is that better? Data science has a caveat. Most of the time, there is a point when more and more effort provides less and less result. There might come a point when cost/reward isn’t worth it anymore. 

Bubble?

Before answering, let’s position the tone of this post. If someone’s investment strategy is buy and sell only, meaning no exit plan, no scaling in or out plan, no take profit plan, and no wondering what to do if it is going south plan, then everything can become a bubble. This means that “standing on the sideline because someone thinks it is a bubble” or “staying in because someone thinks it is not” are both bad plans. Some investors made a ton of money during the bubble ride (they had a plan), and some investors lost a ton of money when there wasn’t a “bubble.” But let’s get to the semantics and determine if AI is a bubble (for extra caution during investing) or not. 

The reason for AI could be a bubble in mass money flow, from institutional to non-financial retail (which is the last chain link in terms of money supply, an indicator of a soon burst). The underlying technology is not understood by 99% of people investing in it, which can’t be that good. This lack of understanding often leads to propaganda, which furthermore “pumps” the stock. Finally, euphoria and accelerating price trends are key (subjective) signs. 

Now if someone looks deeper into the money flow, AI is not the leader; a better computer is, thus we are just on a continuation trend of ever-expanding technological capacity. As said in the first section, AI has been there for a long time; clever architectures have also been there for a long time. What really changed is the cost allocation and computer power. From there, there is a continuation. That’s why it taints the bubble idea. Lastly, everybody would benefit from greater computing power, and that is known at times, not like during the Dot-Com when the internet's future was uncertain. 

So, bubble or not? Will Nvidia have a direct impact on everybody's lives, either through better computing power or AI? Does it deserve to be above there with the magnificent seven? 

Whatever the answer to these questions is, have a plan. 

Disclaimer.

This post is for informational purposes only and does not constitute financial advice or a recommendation to buy, sell, or hold any securities or other financial instruments. The information provided is believed to be accurate and reliable, but the author does not guarantee its accuracy or completeness. You should not make investment decisions based on the information provided in this newsletter/signal without undertaking independent research and, if necessary, obtaining professional advice. The author is not responsible for any investment, trading, or other decisions made based on the information provided in this newsletter/signal.