SITALWeek #392
Welcome to Stuff I Thought About Last Week, a personal collection of topics on tech, innovation, science, the digital economic transition, the finance industry, and whatever else made me think last week.
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In today’s post: Tilting tornados and complex systems; embodied AI with a working model of physics; green's scaling problem; chatty plants; calcium and iron from stars; unexpected impacts of declining birth rates; and, a look at the disappearing sources of outperformance investors have faced over the last century, as it's once again time for the industry to adapt.
Stuff about Innovation and Technology
Tornadogenesis
The 2023 tornado season in the US has been especially destructive. One of the ingredients in tornado formation is a differential in wind speed/direction between the ground and higher atmosphere. This vertical wind shear allows a horizontal, spinning cylinder of air to form. If the air at ground level is sufficiently warm and moisture laden, the resulting updraft and condensation can form storm clouds and (if conditions are right) tilt the spinning air cylinder vertically to form a supercell – a massive rotating thunderhead. As the updraft increases with the strength of the storm, the spinning can accelerate and a funnel can emerge. What struck me in this Wired article on evolving tornado patterns is that we can’t yet determine how climate change will impact tornados because higher temperatures can lead to stronger storms (warmer air can hold more humidity and create a stronger updraft) but also might slow jet streams by lessening the temperature differential between the poles and equator, which can reduce vertical wind shear. The impact of climate change on tornadogenesis seems like a great metaphor for understanding any complex adaptive system: altered forces collide with unexpected, emergent outcomes. The best we can do is be prepared for anything until we learn more about the system and its interactions.
Giving AI a World View
I think the embodiment of LLMs in physical form factors, like robots, drones, cars, etc., will lead to some revolutionary outcomes. The sensors that AI will use to monitor and interact with the physical world will also allow them to mimic life forms by seeking homeostasis – the balancing of their internal state (see AI Awareness for more). Another critical element of the evolution of LLMs is allowing them to have a working model of physics and math, and the integration of Wolfram|Alpha as a ChatGPT plugin (along with other future plugins) should give AI the tools they need to understand the world as well or better than any human. Imagine a LLM that can take in data from JWST and process decades of cosmological research papers; I expect we could see some very interesting new theories of the Universe emerge from such an AI system, allowing us to test those theories and learn far more about how we all got here, and where we’re all going.
Going Green’s Scale Problem
California’s grid is said to need nearly $10B in upgrades to support a greener future. In #384, I looked at the problems Palo Alto was facing with its aggressive electrification push as a microcosm for the seemingly impossible task of upgrading grids nationwide. One of the biggest issues is balancing the daily timing of power demand with power availability as sources shift to wind and solar. Perversely, the current time-of-day electric rate programs in California strongly discourage electricity use when solar is abundant in the afternoon. The conventional wisdom and incentives to charge EVs at night will need to shift to daytime charging at work or public stations (or, at-home chargers could be paired with battery packs; either way, we need a lot more raw materials and infrastructure). IEEE lists a litany of reasons for why the EV transition might be harder than anyone is anticipating. Given the magnitude of the hurdles we need to overcome, I think it would be prudent to anticipate a much slower transition – on the order of 50-100 years rather than 10-20.
Miscellaneous Stuff
Vociferous Flora
Researchers in Israel determined that plants emit sounds at a similar volume to human speech, but in the ultrasonic frequency range that’s beyond the threshold of human hearing. Further, the sounds (which amount to a sort of clicking language, as you can hear in this frequency-adjusted video) vary based on the species and what type and severity of stress the plant is experiencing (i.e., dehydration, pruning). Healthy plants were relatively quiet. What an amazingly noisy world it would be if we could hear all the plants chattering, and yet we might also have more appreciation for their existence and complaints. I wonder if human hearing evolved to the range we consider normal because all the protohumans who could hear the plants chattering away went crazy and died off sooner.
Stellar Bones and Blood
The JWST has captured a very stunning and detailed image of the Cas A supernova, the remnants of an exploded star that is 11,000 light-years from Earth and ten light-years across (that’s just shy of 100 trillion kilometers). NASA reports: “Among the science questions that Cas A may help answer is: Where does cosmic dust come from? Observations have found that even very young galaxies in the early universe are suffused with massive quantities of dust. It’s difficult to explain the origins of this dust without invoking supernovae, which spew large quantities of heavy elements (the building blocks of dust) across space. However, existing observations of supernovae have been unable to conclusively explain the amount of dust we see in those early galaxies. By studying Cas A with Webb, astronomers hope to gain a better understanding of its dust content, which can help inform our understanding of where the building blocks of planets and ourselves are created...Supernovae like the one that formed Cas A are crucial for life as we know it. They spread elements like the calcium we find in our bones and the iron in our blood across interstellar space, seeding new generations of stars and planets.”
Stuff About Demographics, the Economy, and Investing
Despondent Birth Rates
I suspect, at some point in the distant future, humans will look back at today and realize that the increased distraction and social isolation brought about by the infinite content offered by smart phones accelerated, or at least contributed to, the pre-existing trend of declining birth rates. The shrinking of the human population by several orders of magnitude might be the true legacy of Steve Jobs. That said, I am optimistic that eventually technology will fade more into the background and people will begin interacting more…at least until we go 100% virtual and develop the technology to grow babies in vats! I frequently share articles and opinions on population decline, but this pronatalist article has a few new angles to consider. For example, our infrastructure is built for the current city layouts, but, as populations shrink, things get tricky: "an infrastructure collapse is a near constant of any location that has a dropping population. The way we have laid out roads, power, or water infrastructure, for example, is not easily partitioned. If you build out infrastructure in a city to deliver water to a million people and its population drops to half a million, it still costs almost as much to maintain as it did before, with half the tax base and half the benefit." Maybe we will ultimately be forced to abandon certain cities/regions in favor of maintaining and upgrading (e.g., greenifying) more critical areas of population density/industry. Putting into context the extremely low birth rates in South Korea, the article hauntingly states: “It’s as if we knew a disease would kill 94 percent of South Koreans in the next century.” The article also discusses cultural impetuses for increases and declines in birth rates, but a reversal of current trends seems unlikely. Odds are robots could outnumber humans sooner than we think.
Vanishing Edges
Only one-third of active mutual fund managers beat their market benchmarks in Q1 of 2023, according to the WSJ. Bill Miller has often articulated three sources of advantage an investor can have over the broader market: informational, analytical, and behavioral. Miller provides more detail in this letter, but, briefly, I interpret the framework as follows: an information edge is knowing something before others; an analytical edge is having similar information but coming to a different conclusion; and a behavioral edge is acting differently than others despite a similar analysis of similar information. From my perspective, the opportunity for an informational advantage began declining with the onset of the Information Age in the 1980s. Today, thanks to the Internet and ubiquitous access to real-time data (not to mention podcasts, YouTube videos, etc.), I would posit that today there is essentially zero value in investors seeking an informational edge. Analytical strategies to beat the market rose to prominence in the mid part of the last century. I would point to the classic Security Analysis by Graham and Dodd (first published in 1934) as a hallmark for the use of analytical methods to gain an edge over the market. I suspect analytical advantages rose over time (perhaps even fed by the rising use of technology and availability of information), but then they too began to lose value as the machines took over and algorithmic and quantitative strategies rose in both prominence and share of assets, arbitraging away many seeming advantages. LLMs and AI will soon relegate whatever meager analytical edge remains to the refuse heap of ticker tape machines and other investing anachronisms. What then of the last source of advantage, behavioral? Miller’s framework has historically described a behavioral edge as taking advantage of the biases of other humans. That human-focused perspective becomes complicated as passive investing steers past 50% share, and daily market activity is increasingly a reflexive, hyper feedback loop between machines and machine-created information and algorithms. It’s one thing to have a theory of mind for other humans and then to try to take advantage of their biases, it’s quite another thing to have a theory of mind for AI when we don’t even fully know how emergent behavior works in LLMs. Even if we were to recognize that behavioral advantage has shifted from overcoming human bias to overcoming machine bias, soon AI and LLMs will be smart enough to eliminate Miller’s final edge (e.g., see BloombergGPT and Google’s CapitalG investment in AlphaSense). Investing has been one of the earliest professions to be heavily impacted by evolving technology, probably because stock trading is digital and largely information based (the more digital an industry, the more it is susceptible to technological disruption). We would apply the same lens to investing that we apply to any company or industry we analyze: the winners will be the most adaptable organizations that offer the most non-zero-sum outcomes. It’s not entirely clear what the path forward is for professional investors whose goal is to consistently beat the market, but it’s worthwhile to think deeply about the areas to which humans can still uniquely contribute and those that would benefit from adept implementation of AI.
✌️-Brad
Disclaimers:
The content of this newsletter is my personal opinion as of the date published and is subject to change without notice and may not reflect the opinion of NZS Capital, LLC. This newsletter is an informal gathering of topics I’ve recently read and thought about. I will sometimes state things in the newsletter that contradict my own views in order to provoke debate. Often I try to make jokes, and they aren’t very funny – sorry.
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