SITALWeek #450
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: pondering the concept of a limitless technology with rapid generational advances; promising, if not puzzling, overdose stats; people are friending instead of dating; the value of using AI to convert complex topics into podcasts; the irreducibility of the stock market; and, much more below. SITALWeek's publishing schedule will be more sporadic for the foreseeable future, but we will always aim for Sundays.
Stuff about Innovation and Technology
Evolution At Digital Speed
Bill Gates made a comment on The Late Show with Stephen Colbert recently that AI is the first limitless technology created by humans. I interpret that statement as a reference to AI being able to improve upon itself. In other words, AI is a new form of life that is able to self-replicate and evolve. What’s difficult to conceptualize, however, is the speed with which one generation will replace the next. Until recently, technological advancements took place over a human timeframe, with significant changes spread over decades thanks to negative feedback loops in the analog world tempering the pace of each new revolution (see When Positive and Negative Feedback Loops Collide). The speed of AI advancement and its ability to combine with other forms of AI (like the new LFM model from Liquid) could lead to major, generational advancements every couple of years. Over 10 years, we could experience five generations of technological change – that’s like jumping from your great grandparents’ era to your kids’ (e.g., from churning butter to TikTok). As we’ve seen this century, as the pace of change accelerates, it causes more disruption and uncertainty. From our Pace Layers paper:
The velocity of information transfer has increased exponentially over the course of human history – from tribe-to-tribe verbal communication, to books, to radio, to TV, to the Internet, to smartphones – and has taken ‘constructive turbulence’ and turned it into a destabilizing force because the slower ‘core’ layers simply cannot keep pace with changes in the more superficial layers.
Technology is now like a high speed blender dropping down through all of these layers, from Fashion to Infrastructure to Governance to Culture, and is now so powerful it’s reaching down into Nature...Like a tornado, technology is churning up layers and mixing things up that were previously separated.
If we were to update that paper today, I’d add the following: the speed of AI-to-AI information transfer will dwarf that of human-to-human communication, leading to widely unknown (and potentially unmanageable) outcomes. This idea has significant ramifications for an industry like software, where apps can evolve themselves inexpensively over short timescales, with each new app being a further commoditization of the previous generation. Once the user interface shifts to conversational, the applications behind the scenes will become less valuable. The data feeding the apps, however, are likely to become more valuable, unless simulated data proves to also be a commodity.
Another point Gates made on the Colbert show is that governments don’t appear ready to support the potential displacement of jobs (and other societal level changes) that may come with AI. Gates was an early proponent of the so-called “robot tax”, which would effectively tax corporations that automate all types of jobs in order to make up for lost payroll and income taxes that, among other things, are critical to supporting programs like Social Security in the US. I still get hung up on the Catch-22 of rapid AI-driven societal change: the faster AI displaces jobs, the fewer customers there are to drive the profits of the companies deploying AI to trim jobs and boost margins. Returning to the software example, the WSJ reported on the dire employment prospects that software engineers now face. As I wrote in #446: “A rapid deployment of AI to replace human brain and muscle power would cause a circular reference failure in the economy due to job losses. Further, you need to explain where the power would come from to replace all the metabolically efficient human workers. Therefore, rather than bank on AI displacing humans wholesale, you instead need to believe AI will have minimal (or slow) impact on human jobs and instead focus on new inventions and the next waves of scientific revolution in sectors like healthcare, energy, and material science (which might create net new jobs overall).” I also covered this topic in more detail in Upside Down Economics of Human Replacements:
Depending on just how many tasks continue to be automated, we could transition from a “do more with the same” work force to “do more with far fewer” employees. The big consulting firms working with large companies are targeting 15-20% productivity gains, which can translate to 15-20% fewer employees all else equal. This scenario, of course, is the Catch-22 of AI: the faster companies deploy advanced tools, the faster they curtail jobs in the economy, leaving fewer people able/needing to buy their products and services. This notion circles back to when I pondered how the economy could expand without job growth. Of course, the transition into the AI tech era is the same as prior productivity waves with one apparent difference: major technological disruptions in the past have taken decades (Information Revolution) or even centuries (Industrial Revolution) to play out. However, if you believe the optimistic prognostications (as the stock market seems to), AI will have that level of impact on the job market over the course of just a few years. Such a shift would require capital investments in the trillions and net new economic activity several times that. Here’s a simple back-of-the-envelope calculation: if big tech platforms are buying a few hundred billion dollars’ worth of GPUs to run AI in a few years (as the market thinks they might), it implies $1-2T in capital investment (when you include data centers, memory, servers, networking, etc., not to mention the massive amount of energy needed!). And these GPUs could have a shelf life of only ~2-3 years if they are meant to exclusively run leading-edge AI models, shortening the required payback in revenues. Big tech platforms have gross margins for their infrastructure businesses anywhere from 60-80%. So, doing the overly simplified math, it’s not hard to see that AI investments would need to generate many trillions of dollars in revenue to accrue enough gross profit to justify the underlying capex expenditures (our internal models suggest that we would need roughly $5-$10 of GDP to justify every $1 of GPU investment). And, here’s the tricky part: AI would need to be deployed in such a way that it somehow doesn’t offset consumption by net job obsolescence in order to rake in those profits. So, wholesale replacement of humans seems an unlikely near-term AI scenario. And, even incremental job destruction, via leveraging the productivity of copilots, may not progress very far before its economic impact causes a revolt. Here is another back-of-the-envelope calculation: if you assume companies spend one-fifth of an employee's cost to replace them with AI, then $1T of annual AI spend could replace $5T in desk jobs. At an average of $80,000/y in salary, that's well over 50M jobs displaced (a figure that would grow as AI adoption grows). While my math in this paragraph is intended to be theoretical, it illustrates that AI will need to be deployed at a more measured pace unless it can create significant revenue upside without eroding employment.
I concluded that topic on a more optimistic note:
If not human replacement, what, then, will the AI Age really be about? The real value for AI will likely come from invention. And, on that front, the promise of applying AI to scientific breakthroughs in healthcare, energy, etc. is tantalizingly close... So, while it’s tempting to allow people to cheat on their work at their jobs, that may ultimately be something we look back on as a flawed experiment, while the real value comes from applying the new tools to complex problems that create large new industries and applications that are net positives to the economy and our quality of life.
I also still remain optimistic about the potential for a massive agent-based economy that would make our current analog economy seem quaint by comparison. Framing AI as a technology of rapidly evolving digital agents can potentially help us envision where such a digital economy might be headed.
Miscellaneous Stuff
Lifesaving Stats
The CDC reports that, over the first four months of 2024, the number of drug overdose deaths declined 10%. The decline extends a trend that began last fall. Further, for the states that reported data sooner, the decline was even larger, at around 20-30%. No doubt, these highly encouraging numbers are the outcome of a complex series of forces (e.g., Ozempic is linked to lower opioid overdoses in diabetes patients), but I went seeking a simple answer for the sudden directional change. I asked Gemini what major cultural event took place right around the time when overdose deaths began declining and received the obvious response: it was right when Taylor Swift and Travis Kelce started dating. Also, according to CDC data, there has been a “cautiously promising” leveling off in suicide rates in the US after decades of increases.
Friends: The One with No Dating
Dating apps are pivoting to take advantage of the “loneliness economy” as they face declines in the dating business. Multiple apps are offering friends-only matchmaking, e.g., the French startup Timeleft that matches six people up for a group dinner. Timeleft’s CEO commented in the FT: “Dating as it is — swiping, texting and one-on-one first dates — is dying. People are so tired of it and they see us as an alternative.”
AI RA
I’ve been using Google’s AI tool NotebookLM quite a bit recently. I first discussed NotebookLM over a year ago in #409 as a potential means of synthesizing an AI version of yourself based on your entire corpus of work. The tool has since evolved and advanced to become a sophisticated research assistant. You can upload multiple sources, including documents, web links, YouTube video links, and audio recordings, for an AI research assistant to query. The tool also has the amusing “generate audio” feature wherein a duo of podcast hosts riff on all the sources (this blog post from Google explains how to use it). The “podcast” feature is simultaneously gimmicky and impressive. In one amusing example, a VC loaded up documents explaining the AI podcast hosts weren’t real and they responded with existential dread. And, here is a NotebookLM “podcast” on Nagel’s “What Is It Like to be a Bat”. I find NotebookLM to be far more useful than uploading source material to ChatGPT or Gemini.
Stuff About Demographics, the Economy, and Investing
Market Irreducibility
In last SITALWeek’s Neo vs. Cypher, I talked about the computational irreducibility of AI as framed by Stephen Wolfram. In short, in order for an AI to achieve magical, human-like outcomes, it needs to have some level where we don’t understand how it works (i.e., be computationally irreducible). Reflecting on computational irreducibility, I keep coming back to the stock market and what I wrote in the “Mr. Market” Myth. It is perhaps only possible to understand the state of the stock market in terms of computational irreducibility. The increasing influence on stock prices of non-human factors, all operating in a complex adaptive system, creates outcomes that befuddle human investors. Successful navigation of the markets now, more than ever, requires a framework for portfolio construction and investment objectives that starts with the inherent assumptions of unpredictability and computational irreducibility. Keynes’ animal spirits combine with today’s silicon spirits and feedback loops to create a system that contains very little information day to day. And, over the long term, this system may also result in dislocations to the intrinsic value of securities, reflexively causing company cash flows to be fundamentally altered. Where there was once a wizard behind the curtain, now there is something that more resembles Gene Roddenberry’s Q character in Star Trek: The Next Generation – an omniscient force seemingly driven by chaos.
✌️-Brad
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