SITALWeek #390

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: a robot truck loader; why do some robots have backward knees? Google's new Bard chatbot is underwhelming and virtually unusable, will Google let it improve? AI will greatly relieve the burden on doctors; a wonderfully written and romantic glimpse of the most important company in the world, TSMC; communing with an AI artist; the importance of being adaptable in the face of increasing fragilities in the economy due to misguided policies; and, much more below. 

Stuff about Innovation and Technology
Robo Dolly
Slip Robotics has a new pallet-moving robot that can drop the time for loading a semi-trailer from 90 minutes to five minutes with one eighth of the required people. The robot resembles an oversized pallet dolly capable of autonomously moving eight pallets at a time. 

Bard’s Knees
In separate robot news, as I watched this video of the new Agility Digit bipedal robot designed to move items around warehouses, I was wondering why many bipedal robots have knees that bend backward, opposite of mammals. While some, like Boston Dynamics humanoid robots, have human-bending knees, their Spot robot dog has backward-bending knees. I found this article from 2015 stating: “the improved efficiency of backwards gaits stems from lower torque and reduced motion at the hip...In the absence of other information, designers interested in building efficient bipedal robots with two-segment legs driven by electric motors should design the knee to bend backwards rather than forwards.” I decided to put this complex query to the chatbot test given the launch of Google’s Bard chatbot (based on the LaMDA LLM), whose beta I got access to last week. I asked Bard, ChatGPT (both GPT-4 and GPT-3.5 versions), and Bing-Chat why bipedal robots tend to have backward knee joints. ChatGPT-3.5 and Bing-Chat were useful, while Google Bard explained falsely to me that human knees also bend backward, and that’s how we are able to run so efficiently. Ha! ChatGPT-4, however, blew the competition out of the water, providing a detailed, comparative analysis of human and robotic joint movements (essentially, forward-bending knees provide versatility of movement whereas backward-bending knees offer stability for a more limited range of motion). Using Google’s new Bard chatbot more broadly last week left me very unimpressed. The Bard interface seems heavily restricted in the types of answers it gives and is more prone to overly confident hallucinations, even compared to my experience with the early versions of ChatGPT and Bing-Chat. But, these systems can learn fast, so I am curious to watch Bard’s evolution. A key feature Bard lacks is a link to source info (available with Bing-Chat), so I cannot trust any of the output without significant additional research. LaMDA was trained on 137B parameters vs. 175B for GPT-3 (OpenAI has not released the parameter count for GPT-4, but it’s speculated to be many, many times larger). Google also has an internal 540B-parameter model PaLM, which has not been released to the public. 

ER Doc Deficit; AI Scribes
According to STAT, The Match, a program that matches medical students with residency positions, saw a shortage of emergency room doctors for the first time in 2022, with 219 unfilled positions. These ER vacancies more than doubled in 2023 (to 555) as applicants dropped from 4,391 to 3,282. A combination of factors seems to have led to this shortfall, not the least of which is the chaos of the pandemic. As we’ve noted in the past, there is a growing shortage of doctors (and pretty much all other skilled professionals) due to the aging population, and, to a lesser extent, reduced immigration. A compounding factor in the doctor shortage is the increasing bureaucracy the job entails, taking precious time away from patient care. AI is racing to assist the medical field across a variety of use cases, potentially eliminating much of the problematically mundane duties. LLMs can, in some cases, give comparable answers to doctors (and that was before GPT-4, which has a much higher competency). Microsoft, which is keen to add LLMs to every aspect of its business, announced that GPT-4 is coming to its Nuance DAX medical note-taking app, which is used by many doctors. The AI tool, released in 2020, automatically transcribes and summarizes patient-doctor conversations, which, in the current version, are manually reviewed by a human before entry into the patient’s file. The new DAX Express version, currently in beta, eliminates the human checker, sending the notes to doctors in minutes rather than hours. The ability for doctors to automate less skilled tasks, such as note taking or arguing with insurance companies, should help offset the impacts of the pending doctor shortage as the aging population’s medical needs grow. It would seem logical to also task such a system with case analysis to further facilitate medical care and throughput. The DAX scribe software is yet another example from the growing list of incumbent products quickly adapting AI to extend their market-leading positions. The consequences of a “hands-off-the-wheel” approach to healthcare are unknown (but potentially scary, as we’ve found with Epic’s medical software algorithms), and should be approached cautiously; however, such a path led by AI may be unavoidable given doctor shortages.

TSMC in Profile
This new Wired article on TSMC, the most important company in the world, by Virginia Heffernan is one of the most insightful, beautiful, and deeply funny articles I have read. Having spent much time in many of the locations in the article, I found her analysis particularly on point in capturing the slightly surreal aura of TSMC. If you are looking for more history on chips, check out our 2020 article: How a Handful of Chip Companies Came to Control the Fate of the World.

Miscellaneous Stuff
Communing With AI?
In an interview with the FT, author and technologist Neal Stephenson, who coined the term “metaverse” in his 1992 novel Snow Crash (which he described in 2017 as “just me making shit up”), made some comments on AI art: “Scarcity drives quality and AI art is lacking in scarcity, so it tends to be lacking in quality...My theory is that when we experience art — whether it’s a video game or a Da Vinci painting or a movie — we’re taking in a huge number of micro decisions that were made by the artists for particular reasons. In that way, we’re communing with those artists, and that is really important. Something generated by AI might seem comparable to something produced by a human, which is why people are so excited. But you’re not having that awareness of communing with the creator. Remove that and it’s hollow and uninteresting.” The spirit of Stephenson’s statement has merit, but I’m not quite convinced. For one, I don’t think the concept of “micro decisions” is uniquely human given that AI operates in a similar way. I do agree with Stephenson’s point regarding the idea of communing with the artist. I believe that great art offers a glimpse into someone else’s heart and mind, e.g., as Penn Jillette says: “The Beat poet Allen Ginsberg understood that this kind of gamble was intrinsic to great art. He is said to have said, ‘The poet always stands naked before the world.’ I think there’s more to it. The artist must bravely say, ‘I am going to show the world who I am, and I trust that someone will understand.’ Real art, beautiful art, is always a scary act of trust. We look to art to see another person’s heart. That human connection is all that matters. For me, it is a reason to live.” AI is, perhaps uncomfortably, blurring the line between tool and creator. Do we want to see into the heart of an AI so that we might commune with it? Will we find in AI art a quality that speaks to us?

Stuff About Demographics, the Economy, and Investing
Infrastructure Innovation
I was on CNBC last week discussing, among other things, the value in examining the infrastructure layer of the economy, a topic I covered in far more detail in last week's newsletter. 

Rate Rant
At a press conference following last week’s quarter-point rate hike, Fed Chair Jerome Powell said he and the other Fed governors spent the weekend following the collapse of Silicon Valley Bank asking themselves: “how did this happen?” It wasn’t just the words he said, but the way he said them that struck me. I can’t know what his true feelings were, but he came across as openly baffled by what is clearly an easy to understand situation: the largest increase in deposits on record (a result of Fed policy and pandemic fiscal stimulus) followed by the most aggressive rate hike in history (also a Fed decision) caused a situation where 1) deposits are leaving banks to seek yield and 2) long-maturing securities are devalued when sold early to meet withdrawals, causing a shortfall in available capital. Powell’s bewilderment is reminiscent of his comment “We now understand better how little we understand about inflation” back in June of 2022, just as he kicked off the potentially disastrous rate hike cycle without, apparently, even a basic understanding of the system he was meddling with. So, we can now add the effects of rapid rate hikes on the banking system (which is regulated by the Fed!) to the list of economic concepts that baffle Powell. I covered the failure of SVB a couple of weeks ago, so I won’t rehash that, but the Fed’s befuddlement has been on full display for quite some time as they drive important policy decisions by staring in the rearview mirror, a behavior that seems likely to drive the economy off a cliff. By relying on lagging data, ignoring the simple consequences of higher rates on the tens of trillions of dollars of pandemic-created debt and deposits across public and private sectors, and anchoring on an unjustifiable 2% inflation target, the Fed is proving itself to be a potentially harmful weapon rather than an effective policy tool. With respect to inflation targets, we need a greater awareness that if higher inflation is a result of economic growth from investments in things like green energy or supply-chain resilience, then setting an inflation target that is too low will significantly hamper progress and could put the US at a disadvantage to other countries. This type of common sense logic is nowhere to be found in Fed policies.

To formulate a more logical approach to treating inflation (or achieving any other economic goals), it is important to understand the interplay between debt and interest rates in a highly leveraged economy, something so basic that I find most people overlook it. First, you need to start with the self-evident premise that one person’s debt is another person’s asset: someone who loans you money carries that loan on their books as an asset under the assumption you will pay the money back, with interest. The massive increase in debt, fueled by artificially low pandemic rates, became assets on the balance sheets of people, companies, and governments in various forms. By raising rates abruptly, the value of those assets dropped, creating a duration problem whereby banks have lent money out for longer than they should have (to take advantage of the higher rates), thus hampering their ability to meet today’s redemptions by depositors who also want to achieve higher yielding returns on their cash. Further, if rising rates contract the economy, it can compromise the ability of borrowers to repay loans. Continuing to raise rates only increases the risk of existential cracks forming across the global economy. This is such a basic chain of events that it is puzzling that it eludes so many people in power. 

It is also helpful to understand the problem you are trying to address, so you don’t presumptuously treat a scratch by amputation. The majority of current inflation is proving to be a temporary hangover from the pandemic. And, although there are still many sources of potential, long-term, structural inflation to be aware of, such as the declining labor force (the corollary of the aging population), potential deglobalization, or the possibility of expanding global military conflict, runaway inflation remains a low-probability scenario thanks to the real-time information of the Digital Age, which allows the system to self-correct more so than in the past. Moreover, for the last forty-five (and perhaps the last several hundred) years, technological innovation has exerted a powerful deflationary pressure. As we sit here today, AI looks to be a force multiplier on that tech-driven deflation, with significant increases in productivity on the horizon. As such, the long-term trajectory of lower rates remains probable, if not existential. Higher rates coupled with expanding debt cripples the assets of the economy, thus there are fewer reasons to worry about the inflation bogeyman than there are to worry about the collapse of asset values on which the economy is built. As a complex adaptive system, the economy defies precise predictability despite the Fed’s imagination that they can control it exactly with knobs and dials. Some outcomes from interest rate policy should be obvious, but we also know that complex systems can exhibit cascading, non-linear dynamics. The best policy framework (for the Fed and everyone else) is to be as adaptable as possible and, rather than rely on where we just were, focus on the many paths we might take in the future. Regarding inflation, it is far better to use targeted policies to attack the sources of the inflation (e.g., improved infrastructure and logistics, vocational training, supply-chain resilience...). 

One source of potential fragility to keep a close eye on is the ballooning private equity investments (again, fueled by pandemic cash, formerly low rates, and return-seeking behavior) across the economy. The WSJ recently reported that PE investing might be becoming less popular on the margin with institutional investors. Last June, I wrote in The Point at Which Higher Rates Collapse the Economy:
The US private equity market stood at $7T in assets in 2020, with public and private US pension funds comprising just under half of the investors. That $7T asset total (up from $1T in 2005) was quite eye-catching given the high degree of leverage on PE assets – 53% average loan-to-value for 2020 – and the suddenly rising rate environment we now find ourselves in. According to Mother Jones, PE accounted for 6.5% ($1.4T) of the US economy ($23T total) in 2020, and their relative debt exposure is higher than the rest of corporate America. Leveraged private companies is just one risk in the economy to think about as rates march ever higher.
Similar to PE, yet another area of the economy that benefited from low rates, but now faces pressure, is the $2.3T commercial loan market on office buildings, apartment complexes, etc.

The gears of the economy are turning much faster thanks to the Information Age’s high volume, high velocity dataflow. Powell himself noted in last week’s press conference that the SVB bank run took place with unprecedented speed. Increasingly, policy cannot be set in arrears given that the economy will move faster and with more volatility as human hands increasingly come off the wheel of the decision making process. Government policies need to recognize and adapt to the increased speed and amplitude with which the gears of our digitalized, AI-enhanced economy are operating. But, I wouldn’t hold my breath for that. Instead, as investors or corporate decision makers, we should focus on our own adaptability and be ready for anything.

✌️-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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