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: The way we connect to everything on the Internet is evolving at a rapid pace as chatbots become the new engines of discovery. In many ways, the current engines we've become accustomed to feel co-opted and inferior to what they once were. While the Internet we know today took decades to reach this point, the next iteration in AI chatbots could manifest rapidly, as could all of the advertising and spam that go along with it. I also reflect on the seeming lack of context in AI design today. AI is coming for golf announcers. The low-NZS business model of banks and brokerages added to fragilities in the system. SITALWeek will be on break next week, back on April 16th.

Stuff about Innovation and Technology
Discovery Engines
Every business needs customers, and the Internet has transformed the way people find and interact with products and services. For this reason, the biggest Internet businesses created over the last 25 years all involve discovery and advertising. In 2022, according to SEC filings, Google Search accrued over $160B in revenues; Meta’s newsfeed algorithms took in over $100B; Amazon’s ecommerce businesses facilitated online purchase of over $200B in goods and booked $38B in advertising revenues; serving video content, YouTube garnered just under $30B while Netflix (which is just now getting into advertising) tallied just over $30B in revenues. These are all huge businesses that are largely designed to connect users to a seemingly infinite ocean of content, goods, and services. From this perspective, when we talk about “the Internet”, we’re not generally referring to the vast physical infrastructure of fiber, cellular towers, HTML protocols, etc., but rather these giant algorithmic engines that allow us to discover and connect to anything and everything that’s out there in the world. But, underlying the Internet is still that huge amount of physical infrastructure, including hundreds of billions of dollars of data centers and billions of connected smartphones, computers, and other devices. And, that infrastructure has taken decades to roll out. The torch of discovery is now passing to AI chatbots, and that fire is likely to take off far more rapidly than the big discovery engines of the Internet past.

We now take ubiquitous connectivity for granted, but at the time the first iPhone was released in 2007, not only was there essentially no mobile Internet, only 50% of the US had broadband Internet at home (and even that was considerably slower than the speeds we are accustomed to today). Smartphones were adopted more rapidly than broadband Internet, taking about six years to reach 50% of US adults by 2013. While over 85% of US adults have smartphones today, it wasn’t until last year that over 50% of people had access to 5G speeds. At the time of Facebook’s IPO in 2012, only 10% of web usage was on phones, and whether or not common desktop Internet usage (social news feeds, web search, etc.) would migrate to smartphones was still an open question. It’s easy to forget that our use of smartphones has changed in only a decade from practically nothing to nonstop, with online activity (additionally spurred by the pandemic) now plateauing at around 500 minutes (8 hours!) per day for the average American for all forms of digital media. 

As time spent online has grown, content has become essentially infinite, and the business of discovery and curation has become increasingly valuable. Early on, advertising became an important mechanism to help people find – and providers pay for – content. As with other forms of media (newspapers, radio, television), as content and consumption grew, so did advertising. As we went from analog to digital, however, the scale of this relationship exploded, ultimately giving us the broken system we have today of too much content (often low quality or false), misaligned advertising and privacy incentives, and gaming of the system with the search engine optimization industry, viral newsfeeds, spam bots, etc. Today’s Internet is both miraculous and yet extremely disappointing as advertising and spammers have taken over the discovery and content engines. I get filled with a small amount of dread when I have to do a Google search or sift through the Amazon search results, and let’s not even talk about Twitter.  

The first generation of massive discovery platforms, like Google, have dominated the Internet for years; however, with the arrival of AI chatbots, the way we discover and connect with everything will evolve. Chatbots are rapidly becoming platforms, e.g., with ChatGPT’s embedded “plugins”. I’ve been preoccupied with this transition from search- to chatbot-enabled discovery since I wrote AI Companions over a year ago: “As aware agents that know you well and have access to your accounts, messages, and apps, chatbots are ideally positioned to displace the tools we use today like Google search and other habitual apps.” And, given their ability to function as full platforms, I now believe chatbots could take over as the dominant mobile operating system and app store in the near future (or, if that does not happen, they will be entirely embedded in iOS and Android). 

The Internet was a reinvention of the entire customer interface for myriad content and business sectors (before the Internet, we couldn’t access our bank account without a monthly mailed statement or a trip to the local branch!). Chatbots, likewise, will redefine our discovery gateways as we go from multitouch, screen-based systems to conversational interactions with intelligent agents. Indeed, a conversational Internet has the potential to bring about more paradigm-shifting changes than what we’ve experienced over the last three decades. However, unlike the infrastructure-intensive analog-to-digital transition required to bring the Internet Age into being, transitioning into the AI Age with LLMs can take advantage of much of the existing Internet and billions of connected devices, thus allowing for a more rapid revolution (despite its potential magnitude). The biggest gating factor for chatbot adoption is chips. Given how much potential usage there will be for LLMs, chip undersupply could slow adoption by several years, leading to a curve more similar to broadband or smartphones.

LLMs will also change the information landscape, with AI-generated content adding to (and likely surpassing) our current oversupply from social networking and streaming. This impending deluge will make the process of connecting people with content even harder, further skewing our already misaligned incentives and creating scarier and more annoying manipulation and spam. As our 8-hr/day online consumption suggests, the human brain has already been hijacked by the current smorgasbord of dopamine hits and clickbait offerings. So, just imagine when AI can instantly generate intimately customized content to manipulate us. It took decades for the Internet to fully take over our lives and devolve into the morass of misinformation and mediocrity we have today; however, since technological half-lives keep shrinking, we should not be surprised if chatbots are co-opted even more quickly (or, perhaps they already have been). There is a (albeit slim) chance here that AI platforms will develop a different relationship with advertising and be able to defend against spammers. However, it’s more likely, given the high cost to operate AI, that the multi-hundred-billion dollar advertising industry will be needed to pay for it. Maybe we can enable our personal AI chatbots to also consume all the content and advertising for us and face off against spammers, so we can all just get outside and go for a walk instead. 

The Music Matters More than the Instrument
OpenAI CEO Sam Altman was on two podcasts I recently listened to. The first was a shorter, high-level conversation with Kara Swisher, and the second was a more detailed talk with Lex Fridman. While I (generally) hate to make generalizations, I have noticed, as I’ve interacted with AI researchers over the last six to seven years, that there seems to be a lack of contextual awareness in the field. While there are exceptions, I get the sense that AI tools are frequently built just to see if they can be, without stepping back to ask questions like: Why? What are the potential applications? What is the range of outcomes? And, can we help proactively steer the outcome in a more positive direction rather than just dumping the technology in the wild, where it may act like an invasive species? There is a line in the opening of Leonard Cohen’s Hallelujah (a song about dark and tortured love, that is, ironically, often interpreted in a very different way!) that poses the question: “But you don’t really care for music, do you?” That line keeps rolling around in my head as I listen to the folks building AI platforms. There is the AI tool, but then there’s the “music” that will be made with it. Altman, at one point in the interview, tells Fridman that he’s heard of the movie Ex Machina, but hasn’t seen it. Really? If I was creating powerful AI, I’d be consuming every artistic representation of its use that exists in books and movies to understand the why of it, to dream about the good and bad outcomes, and to help shape its course. It’s artists who can see where technology is going more so than the creators of the technology (see The Terror of Knowing What this World is About). AI will be used not just to create content, but to write software, design objects and infrastructure, make important decisions in medical care and education – it could shape nearly the entire future of humanity. So, perhaps, a little more context would be good, since it seems highly unlikely that regulators will step up oversight anytime soon.

Miscellaneous Stuff
Adrift
There were three stories that I found especially troubling last week: one WSJ story concerned the long-term shift in what Americans value (e.g., a growing disinterest in religion/community involvement and in perpetuating the species); an NPR story addressed the decreased life expectancy in the US; and, a FT story parsed the disturbing life expectancy data in the US even further. I’ve been thinking about the consequences of AI (and technology more broadly) mucking with human specialness (e.g., see Giving Up on the Old College Try from 2021). I am once again reminded of the Dalai Lama’s 2016 NYT op-ed about the fear of being unneeded in an increasingly technological world. I am projecting, but I think it’s important (and perhaps even existential) to focus on where humans will add value in a world governed by technology.

AI’s Golfing Takeover
First the golf carts came for the caddies, and now AI is coming for the golf announcers: “IBM and the Masters Tournament, today introduced two innovative new features as part of the award-winning Masters app and Masters.com digital experience, including Artificial Intelligence (AI) generated spoken commentary. Expanding on the popular MyGroup feature — which enables patrons of the Masters digital platforms to watch every shot, on every hole, from all their favorite players — the AI commentary solution will produce detailed golf narration for more than 20,000 video clips over the course of the Tournament. It is the latest example of how IBM and the Masters are working together to create digital fan experiences that offer unparalleled access and in-depth insights into every moment of the Tournament, from the first drive on the first tee to the final putt on the 18th green.”

Stuff About Demographics, the Economy, and Investing
High Rates Spotlight Greedy Banks
In October of 2019, I talked about a particularly negative-sum behavior at brokerages like Schwab: “Most brokers like Schwab make a lot of money taking advantage of their clients' bad cash management choices.” The basic idea is that Schwab has always made it more difficult to sweep cash balances into higher yielding products, but the bank itself lends your cash out at higher rates and keeps the difference in yield they make. Of course, a lot of people know this and actively manage the cash they don’t need right away, but Schwab makes that practice more challenging compared to some of their competitors like Fidelity (which automatically sweeps to higher yield options and makes that cash liquid and available intraday, rather than having to wait until the next day). We try to avoid investing in companies that exhibit such bad behavior because we think higher NZS businesses take share over time from lower NZS businesses. What I of course didn’t foresee is that the rapid rise in rates puts an even bigger spotlight on this business practice at Schwab and other financial institutions. As people and companies now move their cash balances to higher yielding money market funds and treasuries, deposit-based financial firms could see anywhere from a decrease in earnings (as the interest spread they earn is arbitraged by their customers moving money) to a potential bank run like we saw with SVB. You can take advantage of your customers in the name of profits for a really long time, but it creates a more fragile business, particularly as digitalization increases the economy’s transparency and transactional speed.

✌️-Brad

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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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