Friends,
Over Thanksgiving, several family members were talking about how GPT or LLMs were becoming a more regular part of their workflow. In the discussion, someone mentioned they knew an attorney friend using LLMs to automate paralegal work. They still billed the client for 3 hours of work which is what it normally takes. Now this is a double-second-hand story but I have no doubt versions of this are true across the board. Why would it be otherwise? [To quote the late Charlie Munger, RIP]
I’m no economist, but this strikes me as macro deflationary. As I said on the mic last week, technology is leverage — doing more with less. My micro observation is that this lawyer example is a transitionary windfall or surplus that, in the near term, is captured by the producer. But law is a competitive industry so we shouldn’t expect that to hold for long. This bit from last week is an apt analogy:
The run-up to the 2001 dot-com bust was a moment of severe over-earning—a ‘tween moment where there was a boom in trading volumes and speculation to gorge the mom and pops one last time while ringing the dinner bell so loudly that it got the attention of all the suits….
Intelligent firms, knowing the margins were excessive, optimized for market share. They could undercut the mom and pops, offering prices that presented them with a worse risk-reward, although still quite profitable. But the undercapitalized members of the fragmented ecosystem would eventually give up. The surviving firms would increase market share, which meant better looks, which meant better info, which meant more profits, even if the margins were lower. Plow the profits back into technology capex, and you have a flywheel.
LLMs, even if they are “stochastic parrotage”, as economist
has referred to them (without derision, but more of a cheeky reflection of how humans themselves think) are a step-change in productivity for many applications.
If readers were willing to share, I’d love to hear how LLM’s might be altering your workflows or shaping your strategic visions for the future of your work at least in the near/medium terms.
tweeted this:
The lawyer thing feels like a temporary moment of over-earning that competition will sort, but the Sports Illustrated tactic here just confesses “we don’t care” (understandably so, it’s the media equivalent of Betamax).
This brings me to an example of a creator who embodies the opposite — generosity.
📽️How to Make an Internet Shaquille Video (38 min video)
Internet Shaquille has a popular (>500k subs) YT channel that teaches people to cook. He’s an exceptional teacher so I watched this video to discover what he knows about teaching.
Turns out he’s no amateur when it comes to teaching via video. Credentials:
- Went to school for design, got a master’s with a focus on instructional design
- Worked for five years at a public university setting where he was an instructional designer
- Did instructional video-based design for a private construction company for four years
The video is full of wisdom and tactics (I took notes). But my favorite part was his philosophical approach:
A lot of this content is not about how to be successful on YouTube or how to create a popular online course. I believe there’s enough information out there about that, about how to chase the algorithms and such. So, this is more of a Seth Godin head’s perspective, not so much a GaryVee hustle, rise and grind culture type of video. I think that a lot of attention is paid to gaining notoriety, and this is more focused on applying generosity. That’s the word I use the most when I talk about this sort of stuff, and I wish it wasn’t because it sounds like I’m canonizing myself, like I’m this huge saint for making five-minute long YouTube videos. But generosity really is the only way I’ve found to frame this sort of content, like this video that I’m making.
The more generous you can be, the more successful you stand to be as well. I think there’s a direct correlation, if you want there to be.
If you see somebody doing something cool and you say, “Oh, that looks easy. I could do that,” that’s not a very generous interpretation of the cool thing that you saw. However, if you said, “That looks cool. That looks rewarding. I should try that,” I think that’s somebody who’s more likely to produce work in a generous way. And when I say produce work, it’s not about how to make “content”. Content” was always a word for companies like Procter and Gamble to populate their Instagram. You should not aim to make “content”, you should aim to make work that matters for people who care. There’s my first of what will probably be many Seth Godin references. I think that once you start to see your work as a body of work, you’re less likely to write it off as just “content”.
His lessons apply more broadly than making videos. Generous work is anticipating your learner’s actual needs and putting yourself aside to meet them.
If you think the internet is full of garbage today, just wait til Sports Illustrated mindsets really start cooking. Optimistically, I have this feeling that the internet and mass connectivity is still young tech. An awkward adolescent. It won’t “grow up” until its nonsense is so extreme that technology itself equips us with the ability to ignore its cries for attention. Spam as a self-correcting problem.
Unsubstantiated guess — the only viable long game will be a generous one. [There’s always going to be some extractive turd targeting the lowest common denominator, but if tech improves our defenses maybe the worst offenders are enjoying their peak audiences right now. There’s an invisible sense that discourse only goes in one direction — towards hell. But there’s a chance we’ll look back at this time as the frosted tips era.]
Money Angle
Things that caught my attention…
paywalled post:
Universally-Useful Economic Indicators Can’t Last Forever ($$)
It opens with he following example:
The Economist has a bit of an obituary for “Dr. Copper,” the idea that copper prices were a strong leading indicator ($, Economist). Copper’s utility as an indicator makes sense: it’s an input into electrical equipment, housing, cars, and plenty of other durable goods. Since demand for durable goods fluctuates more than other kinds of demand, and since the companies that make these goods need to actually buy the physical copper before they can manufacture the products, it works, in theory, as a leading indicator.[1]
But to use copper this way, you need to imagine an economy where swings in demand for durable goods are a primary driver of the economic cycle. And you also need to assume away any countervailing force. One reason copper broke down as an economic indicator is that the biggest consumer, accounting for half of worldwide demand, is China. And, for a long period that probably ended in the last few years
In another post, Byrne highlights a similar sentiment showing how hard it is to compare data long-term:
Small Caps and Like-for-Like Comparisons
Verdad Research has yet another good piece on the gap between small-cap and large-cap valuations, where they note that the small-cap stock universe is less fundamentally impressive than it used to be. The relative comparison hurts in both directions: larger companies are better-run and faster-growing than they used to be, and investors in small-caps face an adverse selection problem courtesy of private equity firms: PEs will snap up bargains and lever them up enough to compensate for the M&A premium. It’s a good reminder that long-term comparisons between indices are not like-for-like comparisons; small caps got cheap in part because the best of them became large caps and the cheapest got acquired.
I always harp on how markets are biology not physics. On Wednesday I highlighted SIG’s Todd Simkin’s response when he was asked what aspect of trading students have the most difficulty with:
The most difficult aspect, not just for our students, but for our experienced traders as well, is handling the noisy outcome and the noise that comes after the fact. As I mentioned before, the types of people that we tend to hire are those with backgrounds in computer science, physics, finance. However, many of these individuals come from fields where if you can figure out a system, then you can move forward. Biologists are very much in this camp; if you can describe the way biological systems interact, no matter how complex they are, once you’ve described them, you can build on that. You’ve got a description of an underlying process. Germ theory, for example, once developed, everything that can bolt onto germ theory ends up being correct because germ theory itself is a good underlying description of the interaction of germs and health.
But in our world, once you’ve figured out how a system works, it changes the way you behave and once you behave differently, the system itself changes fundamentally. So, we are in this world of constant change and part of that change is our own impact on it. For an astrophysicist, the way a star behaves has nothing to do with whether or not we’re observing it. But for a trader, the way a stock moves has everything to do with our perception of how that stock should move. Once we have an opinion about it, we then go out and do something differently, and somebody else can see what we did and they’re building that into their system and their model of the way the world works. So, dealing with this constant change, I think, is the biggest surprise, especially since we’re bringing in really high-level smart people. We’re not bringing in people who are used to being wrong, and we’re putting them in a world where they’re going to be wrong a lot. Not necessarily in the direction of the trades they make, but certainly wrong in terms if they only evaluated the outcome. Even wrong in terms of having to change their mind frequently, and being open and willing to change your mind and having the right mindset to say this. “This, I think, is correct for now. But it might not be correct tomorrow.” It’s a new experience for a lot of these people who are accustomed to being A+ students, to getting things right. And we’re putting them in a world where they’re not getting a lot right all the time.
Almost every take you see that starts with some comparison of the past and what it should mean for us today materially underweights the biological nature of the system.
This reality is the subtext for the most popular finance post I ever published:
Why Investing Feels Like Astrology (19 min read)
The dynamic in the post is an example of trying to bridge the irreducible paradox of “no, this time is not different” with the plasticity required to incorporate financial actors’ adaptation into the most lindy aspects of your mental model [again, RIP Munger].
From My Actual Life
Lately, when we get a group of people together we have been playing Kahoot!
It’s free [use this link to create an account] although the website tries to make it look like it’s only paid accounts. Your kids probably play it in school already.
There are thousands of trivia-type games (and you can also use it to learn any subject — it is an educational app, but it’s also a technology to create quizzes). Just cast the questions on the screen and use your phone to “buzz” in your answers. You get points for correct answers and the speed of your buzz.
It was a huge hit over the holiday. We did a few Thanksgiving History quizzes before 90s music became the category.
This video walks you through the setup. We had 15 people playing ranging from age 7 to boomer.
Music Rec
Last night, Yinh and I saw Yussef Dayes at the Berkeley Theater. Contemporary jazz, influenced by hip-hop and EDM. The percussion is alien-level, the keys play the role of guitar and there’s a sax instead of vocals.
This 30-minute video is a nice ride:
Comedy
We are binging Avenue 5 on Max. It’s just ridiculous. The first episode is a good litmus test for whether you’ll like it or not.