SITALWeek #437
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: Walmart's healthcare experiment exit portends bigger problems; a NIMBY backlash is forming against data centers; the surprising places you hear loons; more advice from Kevin Kelly; the middle-class hiring slump; forecasting the weather; and, much more below.
Stuff about Innovation and Technology
Walmart Yields
Walmart announced a surprise exit from their five-year-long effort to disrupt the healthcare industry. Fifty-one clinics and their telehealth centers will cease operation (their pharmacy division will continue). I’ve had a certain fascination with both Walmart’s and Amazon’s attempts to expand into healthcare. As the largest employers in the US (besides the Federal government), they were in a unique position to experiment with different ways to improve the health outcomes of their own employees, and potentially create a better mousetrap for the doomed American healthcare system at large. I covered the opening of the first Walmart healthcare supercenter in 2019 and their expansion into insurance in 2020. And, back in 2021, healthcare was central to Walmart’s ambitions of being a broad “lifestyle app”, despite reports of the company scaling back their goals in the sector. Walmart cited a combination of reimbursement problems and high operating costs that made medical care unprofitable. For a company that competes effectively in the largest, most competitive segments of retail, that’s quite a statement to make. Further, Walmart is more willing to make major, multi-decade investments in new areas compared to the typical company, so this failure seems doubly significant. Beyond a handful of small neo-insurance companies, I am not aware of any large-scale ongoing attempts to disrupt the healthcare leviathan and improve patient outcomes. Given that every unstoppable force is giving up against the immovable object of US healthcare, we may now be reliant upon AI, and technological innovation more broadly, to make inroads into improving patient outcomes, service levels, and costs. On that front, just last week, Google announced a medically tuned version of Gemini that scored even higher than GPT4 on multi-modal queries (multi-modal here refers to multiple forms of input, such as radiographs or pictures of a wound). The paper on the model can be found here, and this string of tweets has the stats and some examples. I might suggest (mostly tongue in cheek) that perhaps Walmart’s exit from healthcare reflects their view that GLP-1s will make everyone healthier (Walmart has reported shoppers on GLP-1s buy less food) and AI doctors will reduce the cost of medical care, thus preempting Walmart’s need to disrupt the industry. Let us hope.
Pushing Back on Electrons
Blowback is increasing against the number of large data centers being built to power hungry AI, as these massive installations can strain the grid and make energy more expensive for everyone. We’ve previously discussed this energy issue in Probabilistic Fortune Tellers and Pushing Electrons. Further, we’ve discussed the tension between the insatiable growth of digital technologies and the slowly moving gears of the analog world in When Positive and Negative Feedback Loops Collide. The WaPo reports that efforts are underway in multiple states across the US to slow or curtail data center construction. And, Bloomberg reports that Dominion Energy has received the equivalent of several nuclear power plants worth of energy requests from developers of new data centers in Virginia. Given big tech companies have not shied away from direct energy investments in the past, it's unclear to me why more of them aren't actively pursuing locations and permitting for new nuclear power plants today.
Miscellaneous Stuff
Loon’s Tune
I don’t think I could point out a Common Loon if it bit me in the face, but I do know what it sounds like, thanks to pop music. This article reports on the ubiquitous presence of the Common Loon’s ululating tremolo call and haunting wail in recorded music/media over the last few decades.
Just Try Stuff
It’s Kevin Kelly’s birthday, so it’s time for 101 more bits of bite-sized advice – always popular with SITALWeek readers. For those who missed it, Kelly compiled prior years’ advice into a book.
Stuff About Demographics, the Economy, and Investing
Blue and White Hiring Gap
One hypothesis I am tracking closely, as large corporations embrace AI, is the potential for casualties in white-collar information-based jobs. While we may not see outright job destruction, it’s possible that companies will grow with the same number of employees, or hire at a slower rate in the future, thanks to productivity gains. Such trends, of course, are nothing new. Tech has been an accelerating productivity driver over the last fifty years; however, approaching the point where computers can reason as well as humans portends an even larger leap forward in efficiency. As Microsoft trains its AI on all of our mouse clicks and keyboard taps, it’s gearing up to automate all our rote work, and perhaps even some of our more creative tasks. This productivity boost will likely result in fewer people clicking and tapping (per every dollar of new revenue growth) at many companies. As a massive aggregator of retirement investment accounts, Vanguard has a unique vantage point on hiring trends. BI reports that Vanguard is seeing a major slump in hiring for the top third of earners (>$96K annual salary). The hiring rate for this group of workers, who we might refer to as the heart of quintessential middle-class households, peaked in 2022 and is approaching its pandemic lows, well below pre-pandemic levels. Meanwhile, the hiring rate for the bottom third of earners (<$55K annual salary) remains elevated above pre-pandemic levels (it should be noted unemployment still remains near record lows for all salary levels). While correlation with tech-based productivity gains doesn’t prove causation, this type of income-based hiring data is an interesting stat to keep an eye on.
PDS
As I mentioned last week, I’ve been live-tracking YouTube weather chasers as they intercept dangerous storms ripping across North America’s Great Plains. There is an official term used by the National Weather Service to warn the public of storms with the potential for significant damage: PDS, which stands for a “particularly dangerous situation”. While this formal term describes very serious circumstances, I do find it a little funny. It sounds like it’s trying to overachieve. Nonetheless, if you are in a PDS, you need to seek shelter immediately – in the lowest, most interior place possible of the building you are in. Sometimes, as investors, we come across a sector-based PDS (which tends to occur far more often than the deadly F5s that can engulf the entire economy). I am reminded of a moment in time, nearly 25 years ago, when I found myself in the middle of such a particularly dangerous storm. In the fourth quarter of 2000, the sales for communication equipment peaked. This peak took place several quarters after the collapse of the dotcom bubble. The dotcom bubble was built on the hope that the comm equipment infrastructure cycle would build out the Internet, thus opening vast new markets in commerce and media (i.e., create the digital world as we now know it). Back then, there were no smartphones (there was hardly even any cellular data!), and the Internet was largely text-based and operated at slow dial-up speeds. But, there was hope and dreams aplenty, and the comm equipment industry was close to a $200B run rate in annual sales across the supply chain at its peak. In the fourth quarter of 2000, I was busy polishing my shiny models filled with (over)confident predictions about future revenue. (Back then, as a newbie analyst, I labored under the misconception that my economics degree was actually applicable to the real world – what a hoot!). Anyway, I was forecasting a “conservative” deceleration from 200% growth to 80% growth, as a proper analyst should. And, then the storm came. It was a sudden, massive, PDS. I needed to seek shelter immediately. For one well-known, high-flying company at the time, my forecast for revenues just four quarters into the future ended up being ten times too high. Roughly speaking, I forecast growth of 80% to around $500M in revenues. Revenues ended up clocking in at around $50M just twelve short months out. (And, this was not some dotcom vaporware company, it was a real company that had been around for decades selling high tech components into many end markets). I can still vividly recall the awesome disconnect of a one-year prediction being off by an order of magnitude – it was an out of body experience. At the time, had I possessed the vocabulary for it, this would have been my first encounter with the folly of trying to predict the future inside a complex adaptive system. It was, to say the least, an informative event, and it set me on the long path to figuring out a better way to conceptualize and invest in the economy – the upshot of which was Complexity Investing, which Brinton and I published in 2014. Lamentably, I still lack mystical fortune-telling powers; however, because I know what kind of turbulence I can expect from a wildly chaotic system, I am much less prone to being blindsided by economic turmoil and stock market bubbles. Perhaps one day we will create an all-powerful probabilistic AI fortune teller capable of predicting the markets’ ups and downs, but that day has not yet arrived. Circling back to tornadoes, it seems that highly accurate, AI-driven weather forecasting is much closer to reality. Yet, even the best models are still far from infallible. That tornado I linked to at the top of this paragraph was a storm I watched happen live, with the funnel touching down a full 10 minutes before the NWS issued so much as a warning (let alone a PDS). The lesson here I think is two-fold for weathering any type of storm: 1) always have access to a safe space, and 2) make sure you are looking in the right direction; you might think the storm is up ahead, but it could be funneling down on you from above. And, always lean on those lessons from complex adaptive systems.
✌️-Brad
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