To Our Investors and Friends,
November was a volatile month for the market, which sold off in the first two weeks and then rallied in the last two weeks to close almost flat. The Magnificent Seven ended down almost 2%, while the S&P 500 finished up .1% in the month. The 10-Year Treasury bond declined 9 basis points to 4.0% and the 2-Year Treasury note dropped 13 bps to end at 3.5%. Oil retreated 3.8% in the month to close at $59 a barrel. Healthcare stocks rallied by almost 10% as the technology sector fell 5% in the month. This helped the Russell 2000 Value increase by 2.8% and the Russell 1000 Value to expand by 2.7%. The tech-heavy growth indexes gave up earlier gains as the Russell 2000 Growth retreated .7% and the Russell 1000 Growth declined 1.8%.
The stock market rally this year has been largely tied to investment spending on Artificial Intelligence (AI) Infrastructure. This has led to significant gains in both the spenders on AI – the Magnificent Seven has risen 23% YTD - and the beneficiaries of that spending, represented by the Philadelphia Semiconductor Index, up 41% YTD. Many speculate that without this significant capital expenditure on new AI data centers, the economy could have gone into recession. For the first time in months, however, investors are beginning to question the returns on investment into this infrastructure, and for good reason.
In her book, Empire of AI, author Karen Hao sheds light on the capabilities and shortfalls of the deep learning Large Language Models (LLMs) that are being developed to create the popular AI tools being used today. According to Hao, “deep learning’s success is largely because very little investment went into exploring other paradigms. While neuro networks are remarkable inventions, with myriad exciting uses, there are weaknesses, namely their hotly contested and inefficient ways of storing accurate information and reasoning have endured as companies have deployed them in an expanding list of contexts and applications. Neural networks have shown, for example, that they can be unreliable and unpredictable. As statistical pattern matchers they sometimes hone in on oddly specific patterns or completely incorrect ones.”
Hao further explains that such problems as hallucinations will not go away and highlight the core shortcomings of the technology. She explains, “even the term hallucination is subtly misleading. It suggests that the bad behavior is an aberration or a bug when it is actually a feature of the probabilistic pattern matching mechanics of neural networks.” Hao further explains, “the issue of hallucinations was rooted in the nature of neural networks. Unlike the deterministic information data bases of symbolic systems, neuro networks would always traffic in fuzzy probabilities. Even with Reinforcement Learning from Human Feedback (RLHF), which helped to strengthen the probabilities within a Deep Learning model that correlates with accuracy, there was fundamentally a limit to how far the technique can go. The model obviously has to guess sometimes when it is outputting a lot of detailed factual information. No matter how you train it, it is going to have probabilities on things, and it is going to have to guess sometimes.”
At Kingsland Investments, we view the advancements in AI as early and flawed, but useful in many applications. As with any technology, it will take years and meaningful advancements before it has a hope of being truly intelligent. Speculation on when intelligence emerges is anyone’s guess. What we question is not that investments should be made in AI, but whether the hundreds of billions spent in pursuit of improving today’s flawed technology could be better spent elsewhere. We believe the market is questioning the same thing, suggesting that the significant returns in a handful of names is now at risk, while other proven technologies offer much better future returns. With this in mind, we will seek to make investments in companies that are demonstrating real benefit to their customers with new technology through better functionality at lower cost.
All the best to you,
Arthur K. Weise, CFA
