It’s valuable to understand the lifecycle of things (peoples’ careers, companies, quarterly planning cycles, etc) because there are often discontinuous jumps in understanding/legibility which one can get ahead of by being a bit more willing to put in the work than the median is.
This is probably not a huge source of alpha in e.g. publicly traded large cap companies because there are already a lot of smart people spending an appropriate amount of effort around quarterly earning announcements, but there are a lot of other lifecycle events in the world.
It’s borderline crazy that it took tech companies decades to understand that graduation changes functionally nothing and therefore you should be as willing to talk to someone six months before as six months after.
There are many more things shaped like that.
Broadly look for things which allow bootstrapping signal, look for things which allow getting ahead of gatekeepers, look for things which enable second chances and side doors, look for bets with outsized potential to the upside, and look for bets which don’t sound good at dinner.
“Bets which don’t sound good at dinner?”
A lot of investors express the desire to be contrarian, but they’re contrarian in a way which makes for sparkling and attractive dinner conversation, which is probably just about the least likely place to find contrarian alpha.
“What’s a bet which doesn’t sound good at dinner that you’d nonetheless be happy investing in?
Most business software in the world is written by a fundamentally inefficient capital stack. It wants a tiny layer of capital followed by cheap debt to operate and scale. I.e. PE.
That’s at least five different investing theses in a tweet and while they might be wrong they do have the advantage of being so utterly snooze-inducing that only people who care an irrational amount about B2B software can stand thinking about them for more than 30 minutes.
(This one actually might be quite popular these days in the right circles; got to keep moving to find the boredom frontier :) )
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This week on Complex Systems I'm joined by... Claude Code?
I think people who don't program professionally extensively underrate the discontinuous advance in productivity engineering is going through. So we step through real eng work, basically verbatim, with me commenting.
The specific business problem presented is a real one which a real business (mine) actually lost money over: transient payment failures in collecting annual memberships for Bits about Money. Analogous problems bite almost every Fortune 500 company, to tune of billions.
They largely go unsolved because the problems are illegible to the parts of orgs which are not payment experts. For the parts of orgs which are, like Business Operations or Payments teams, this is not salient enough to draw executive attention to get engineering hours.
“I spoke with 21 billionaires” is historically the sort of flex you could only imagine in the top of tier 1 media, and ironically I think they’re probably least capable of it today, after a few years of burning karma wantonly.
Many of the emails will say “I just want to hear your side of the story” and many of them will even actually mean that and come from reporters who respect sources and promises they’ve made to them.
But other emails said the same words and then did not follow through.
One of the reasons Solana can do this is he has a persistent reputation in the ecosystem and everyone knows it. This historically was true for some institutions, but during a rough period for them they developed principle/agent problems.
Odd Lots has a really fantastic episode on why Claude Code matters, and while it is likely not directly useful for you if you follow me, it is the single best artifact I’ve seen for that smart person you want to quickly educate about this.
* How giving LLMs capability to write Unix commands gives them deterministic access to ~60 years of powerful, composable software capabilities
* LLMs are quickly becoming the “interpretation layer” and a lot of work is that, at varying levels of abstraction
* Says a really important takeaway that most of the world has not internalized: this fundamentally transforms a field/craft in a way which predictive autocomplete was not going to.
In many domains a generalist who is good at AI and puts an hour or two into something will be three to four sigma from the mean entrant into a support / escalation / etc inbox.
Mitchell has an example from bug reports; I can easily imagine examples from e.g. financial issues.
I think *once* when doing advocacy work for people with banking/credit problems I ran into someone who had an organized call / letter log and so could cleanly generate a timeline that the financial institution could match up with their own files (and obligations).
Try it if you don't believe me but if you give AI a bunch of unstructured input like most people's impressionistic account of how this has been so frustrating dealing with the bank, they will frequently redigest it into "Here's a timeline with bullet points."
Considering writing about non-coding LLM workflows a bit in December partially for personal interest and partially so people can see concrete examples of progress / usage.
The one easiest for me to talk about is just a geeky hobby: here's a plastic model and then here is ChatGPT producing a painting reference of ~that model, after a discussion on characterization, color scheme, etc.
I honestly love using it in my art projects. Hallucination rate is acceptable given ~wide acceptance criteria in art; like Bob Ross used to say, there are only happy accidents if e.g. its suggested recipe for mixing a teal paint does not actually result in teal immediately.
If I clipped every good Byrne Hobart or Matt Levine line I’d never get around to writing my own stuff but this from Byrne is too good to not share:
An extraordinary fact about finance is that there are some firms which are financial service providers specifically for scams which sometimes, almost as an industrial accident, bafflingly end up in a contractual relationship with a legitimate, successful company.
These underwriters are not necessarily that; some overlevered highly “structured” IPOs of midmarket software businesses should have a non-zero price, and a capitalist should not say they are a scam just because he is not a buyer at that price.