CHG Issue #111: Change
Today’s market offers us an excellent case study on how change manifests in the economy and the markets, which we recall are two distinct and independent (and potentially co-dependent) domains. Yes, we are finally going to talk about the recent AI boom which started late last year with the public launch of ChatGPT and recently had gasoline poured on the fire by Nvidia.
The revelation of the application of technology that has been around for many years which occurred with the release of ChatGPT is a classic case of the diffusion of innovations model at work. As any tech entrepreneur knows it is not just the technology but the combination of the technology and the ability to commercialize it that matters. When ChatGPT came on the scene it opened Wall Street’s and a good chunk of the public’s eyes to the myriad of potential commercial applications for generative AI, which is just a small subset of AI technologies. In the diffusion of innovations model, which was pioneered by Everett Rogers, this revelation falls squarely in the early majority phase of the cycle when mass adoption first starts. Malcolm Gladwell’s book Tipping Point describes the “mysterious” sociological changes that occur in everyday life through the lens of the diffusion of innovations model. The tipping point is the “moment of critical mass” which is thought to lie between the early adopter and early majority categories in the diffusion model. We have just seen a tipping point in the adoption of AI.
There are biological mechanisms at work that dictate how change manifests in society. When we are born, we come with very little pre-programming and must learn how to interact with and survive in the world around us. We do this by trial and error in a constantly repeating feedback loop. Touching the hot stove and burning your hand is a bad outcome so we learn to not touch hot stoves. Working hard and getting good grades leads to praise from our teachers and parents which is a good outcome, so we learn to work hard. As we learn and repeat these things neural pathways are formed in our minds which are reinforced by repetition. This is how we form good and bad habits, and it is also how we train the neural networks behind AI.
Using this biological framework for how small change can lead to lifelong habits we can better understand the recent AI craze that has rippled through the markets. Just like any innovation the release of ChatGPT got a lot of initial attention and excitement. The trial balloon was floated, and it was able to establish and maintain altitude, but it was still a relatively isolated thing. The innovators had released the trial balloon and now the early adopters were at work and were finding success, so the balloon kept rising. During this time everyone is trying new things, receiving feedback, learning, and adjusting. The longer something hangs around and people don’t run away because they haven’t been burnt by it the more lasting the change becomes. However, this is only happening for a small subset of society as we haven’t reached the tipping point yet.
A few weeks ago, the narrative for the Nasdaq changed. First it was PTJ who came out bullish on stocks because he believes the Fed is done raising rates then Steve Cohen said we should ride the wave of AI and stop focusing on a recession. Now that we had two authority figures giving the market and AI specifically their blessing the perceived risk of any innovation which holds the majority back from adoption was reduced and the momentum of the innovation was strengthened.
Change happens slowly at first and rapidly picks up speed as adoption in society increases. Kind of like bankruptcy is said to happen slowly then all at once. Just like the laws of physics tell us that an object in motion stays in motion until a counteracting force is applied, we tend to do the things that we have most recently done regardless of the outcome. This is an important distinction as we don’t always learn the right lessons from outcomes and don’t always do what is in our own best interest. The power of familiarity overtakes fear, and we plow ahead undaunted.
When Nvidia issued an unprecedented increase in revenue and earnings guidance the markets were in a very precarious state and vulnerable to a violent and emotional move either up or down and that is what we got when Nvidia gapped higher by ~25% in a single day. It is at this point when fear and greed take over and we start to move quickly through the diffusion process.
We also have the model of the emotional cycle of change to apply here. As things move quickly the emotional responses of fear and greed take over and cause this well-known cycle. We saw this in the internet bubble in the early 2000s where the craze drove many companies that are not in existence today to very high valuations in the uniformed optimism stage of the emotional cycle. To work off that excess we had to go through the valley of despair to clear out all the inventory that had accumulated in weak hands before we could rise back to the old highs and eventually exceed them, in terms of the technology and the market valuations.
You can apply these models to anything and over any timeframe. Change is at the heart of the markets and understanding how that change manifests is what allows us to profit from it. Change can take place for you personally or for society as a whole with different logistical and emotional considerations but the biological process for all of us is the same.
The rally we have seen over the last few weeks is structurally very weak, but it is like the trial balloon that is floated by the innovators. We have broken out of our trading range and the buying looks like short covering, and it doesn’t appear to have the support of a broad cross section of market participants. Steve Cohen shared a lot of wisdom in his remarks that we should stop focusing on the potential for a recession and instead focus on the present moment which is the boom in AI. We don’t know if or when there will be a recession. We don’t know if this weak upside breakout will put pressure on real money who is underinvested and not participating in this rally causing them to capitulate and cover their underweights. But we do know how to measure the acceptance or rejection of this trial balloon. We know that we are at the beginning of a potential change point in the market, and we can go with it knowing that our downside is limited due to trade location since this breakout could be the innovators acting. One the other hand this breakout could be the laggards, and this could be the final innings of the weak rally, the “blow off top” that many have been looking for. No one knows what the future holds but operating in the present and understanding how people respond to change can give you an edge in life and the markets.