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: It appears that my long lamented desire for general-purpose robotics platforms is finally being realized, and we are poised to see accelerated innovation and disruption from the combination of AI and robots; the importance of resetting to a beginner's mind to analyze things that we think we already know well; solar geoengineering; electric bandages, and some updated thoughts on Silicon Valley Bank.

Stuff about Innovation and Technology
E-Bandage
Electroceuticals use electricity to promote healing throughout the body. One group of researchers is working on zapping bandages that can deliver pulses to hard-to-heal wounds, which so far have been shown to speed up recovery by around 30% in diabetic mice. Inflammation can disrupt normal electrical cell signaling (that seems like a flaw in the human operating system), and, theoretically, external electrical pulses allow cells that speed up healing to reach the wound in greater numbers. The concept device is powered by energy-harvesting coils, and it communicates progress (based on conductivity of the wound, i.e., dryness/healing) to a smartphone. 

LLMs Transforming Robotics
Over the last few years, I’ve lamented the slow arrival of general-purpose robots, but I think a major change is now taking place thanks to LLMs. I wrote about Microsoft’s efforts to embed ChatGPT into robots, drones, etc. last week in #387. It turns out that Google is fairly far along in embedding LLMs into robots as well. Google's PaLM-E robot demonstrates what’s known as positive transfer, which means it can learn one skill and transfer that knowledge to other tasks. Further, “the researchers claim that PaLM-E exhibits emergent capabilities like multimodal chain-of-thought reasoning (allowing the model to analyze a sequence of inputs that include both language and visual information) and multi-image inference (using multiple images as input to make an inference or prediction) despite being trained on only single-image prompts. In that sense, PaLM-E seems to continue the trend of surprises emerging as deep learning models get more complex over time.” PaLM-E is an interesting form factor consisting of a one-armed, wheeled robot that seems capable of efficiently mimicking a lot of human movements. The paradigm shift here seems to involve three things: 1) learning/training, 2) user interfaces, and 3) robot-to-robot interactions. Embedding LLMs in robots of all types allows them to learn and adapt on the fly using language as a translational layer between their physical forms and the real world. Further, LLMs allow us humans to direct and correct robots much more easily. Lastly, given that LLMs can act as universal translators, robots will be able to interact with other robots and cloud-based LLMs in complex ways. There is a loose analogy I’ve been using for this shift (which is meant to just be an illustration rather than literal): instead of programming an autonomous vehicle to drive safely on roads, you can put an LLM-enabled humanoid robot behind the wheel and ask it to learn how to drive like a human (or, ideally, better because it won't be live-streaming on TikTok). It may seem subtle, but this shift – from a single-purpose form factor trained on a single task to a general-purpose robot that can train itself based on real-world knowledge/input – is a vastly different mental model for thinking about robotics and automation and should greatly accelerate progress. The emergence of LLM-based, general-purpose robotic platforms is likely to be one of the biggest impacts to the global economy in the decades to come, with great potential to easily reach billions of units. 

Mentally Reformatting for the AI Age
Given the potential speed at which AI is developing and the wide range of impacts it could have on every industry, a beginner’s mind analytical approach is merited, especially for established industries. Accordingly, I am trying to reexamine all beliefs I’ve held as truth for a given industry/company in light of advancing technology. The goal is to start from scratch, assuming no a priori knowledge, and examine the range of potential outcomes for everything. I was thinking along these lines as I contemplated the software industry and the ten-year time horizons of many VC funds (since power-law winners have typically emerged in the later years). Many existing software apps, which are largely sold on a per-seat or usage basis, might need to transition to being sold on a value basis, i.e., factoring in the customer's cost savings for the employees that an AI app replaces. This is something we are already seeing in robot pricing, as mentioned in #364. There is growing evidence that adding AI software tools affords ~50% productivity increases, which I would estimate might imply somewhere around 30% fewer seats for those tools, ceteris paribus. Of course, the real-world changeover will be far messier and less direct, and many people will be repurposed to other, higher value tasks. Does the rapid pace of AI development also call into question the decade-long time horizon of VCs? (Or, for that matter, many other types of growth asset investing?) A widening range of outcomes can be accommodated with a larger number of smaller investments. This is a key lesson we took from VC investing and complex adaptive systems as described in our paper Complexity Investing. But, I wonder if, now, an even more extreme version of this long-tail investing approach is merited, in combination with more frequent assessment for cutting bait and reinvesting as the disruption cycles compress. I think this foreshortening has already happened in media investing, as proliferation of content (combined with fickle consumers hopping from one digital cigarette to another) has made it harder to deploy capital. Digital transitions increase the speed of disruption and widen the range of outcomes, and AI could amplify that trend far more than what we have seen from the Internet, cloud computing, and smartphones over the last twenty-five years.

Miscellaneous Stuff
Solar Geoengineering
We know from historical records of large volcanic eruptions that the ensuing airborne ash and smoke can substantially cool the Earth by reflecting solar radiation. Similarly, we could engineer methods of reflecting back sunlight and/or make it easier for heat to escape Earth’s atmosphere. Solar geoengineering could have vast and unpredictable effects, like shifting weather patterns or ocean currents (or Snowball Earth, in the extreme!), but, according to researchers, blocking just 2% of solar rays could entirely offset the warming that has resulted from carbon dioxide emissions from the last couple hundred years. Such an undertaking would likely require agreement across the globe, which seems unlikely, and could result in rogue efforts to control the weather.

Stuff About Demographics, the Economy, and Investing
Rate Hikes Trigger First Quake
The breathtaking collapse of Silicon Valley Bank highlights the complex adaptive system of markets and the economy. In the case of SVB, it was an interconnected set of circumstances that led to its fragility, which proved fatal upon exposure to rapid rate increases. Compared to a typical bank, SVB experienced a series of events that interacted in unexpected ways, starting with the massive stimulus during the pandemic. This overfunding of the economy led to excessively risky return-seeking behavior by individuals and institutions. One such risky bet was a large increase in venture funding, much of which was deposited in SVB accounts (largely corporate but also individual, as founders and employees cashed out on bubble valuations). This flush of deposits resulted in SVB seeking yields that were higher than the near-zero short-term treasuries available during the pandemic. Namely, SVB invested in assets with longer term maturations, which were inherently more sensitive to changes in interest rates. Under normal circumstances, that might be a logical decision. But, in a situation of unprecedented and surprise rate increases, those securities were worth less near-term than when held to maturity. That’s also fine if you don’t have excessive withdrawals, but rate hikes also punctured the risky asset bubble, leading companies to begin withdrawing more cash than what they were depositing as they funded losses and raised less new capital. I take no solace in the fact that I highlighted this issue at SVB in a section titled Pop! back in October, and, while it was within the range of outcomes, I certainly did not anticipate the bank’s dramatic unraveling. 

As to whether there is a broader mismatch between bank deposits and the maturity duration of their assets, we’ll see. The FDIC noted around $620B in unrealized losses on bank holdings at the end of 2022 (it’s not clear if those have risen as rate expectations rose as well, or if banks have taken actions to rebalance their portfolios). Further rate increases to fight the inflation bogeyman would likely cause these paper losses to increase. Thus, while SVB might have experienced unusually high outflows owing to its preponderance of cash-strapped VC customers, it's not alone in having a large base of uninsured deposits. One estimate puts uninsured deposits (those over $250,000 per account) at roughly half of the $17T US bank deposits. Further, around one third of bank deposits are in smaller/regional banks, which, in some cases, might carry fewer reserves. And, thanks to higher rates, individual and corporate depositors are increasingly withdrawing regular bank deposits in order to invest them in treasuries or money market funds carrying far higher yields in an attempt to protect cash holdings from the impacts of inflation. In short, the Fed’s blanket attempt to fight inflation with rate increases has negatively impacted current valuations for bank assets while simultaneously incentivizing investors to withdraw deposits to invest in higher yielding assets, creating an elevated risk for bank runs and panic. Thus, it’s no wonder, that the government announced a special vehicle called the Bank Term Funding Program to loan money to banks experiencing a mismatch between deposits and held to maturity investments. While I appreciate the government's efforts to address this legitimate bank-run risk, I can’t ignore that it that stems from repeated policy errors over the last few years, starting with over-stimulating the economy and then increasing rates far too rapidly. Behind both of these missteps are the Fed’s overreliance on a rearview-mirror economic perspective (with data that are often heavily lagging reality) and precise economic forecasting, despite the fact that no one can predict the future, including, and perhaps especially, the economists. A more appropriate set of policies would treat the sources of inflation (labor shortages and outdated analog infrastructure to name a couple) rather than using the blunt tool of rates to reduce employment and spending. But, given our current situation and the policy makers’ antiquated approach, the best expectation might be a series of policy mistakes and government bailouts for the foreseeable future. As always, expecting the unexpected and preparing for a range of outcomes, all the while focusing on adaptability, is the best way to navigate complex adaptive systems like the economy. As I concluded in last October’s post on SVB: “the Fed is planning...an unprecedentedly steep increase in rates in short order, just as things are beginning to unravel. Hopefully, it will only take a few modest quakes to reacquaint the Fed with reality, and we can thus avoid the big one.”

✌️-Brad

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