## Doing Business in the Dark Forest

The Dark Forest Hypothesis is a recently-repopularized answer to Fermi's question: if we know life can arise on at least one planet, and we know that there are a lot of them, isn't it suspicious that we aren't hearing any radio broadcasts from them? Various answers have been proposed, but one of the ominous ones was popularized in, and named by, The Dark Forest: intelligent life forms have run the same calculation, and think that if they're detected, the species that detects them will worry that they're hostile and launch a preemptive strike—and that it's a good idea to constantly look out for signs of other species in order to get them first.

When I read about it, the first thing I remembered was this Paul Graham essay: 

>If you ever do find yourself working for a startup, here's a handy tip for evaluating competitors. Read their job listings. Everything else on their site may be stock photos or the prose equivalent, but the job listings have to be specific about what they want, or they'll get the wrong candidates.

>During the years we worked on Viaweb I read a lot of job descriptions. A new competitor seemed to emerge out of the woodwork every month or so. The first thing I would do, after checking to see if they had a live online demo, was look at their job listings. After a couple years of this I could tell which companies to worry about and which not to.

That trick still works at companies that are big enough to have a substantial list of jobs, and news stories occasionally break because a job listing shows that a company is planning something it hadn't previously discussed, like Apple hiring someone to build a demand-side platform or Duolingo hiring someone with a background in music education before they'd announced that product. You could also use them to see which e-scooter companies thought they’d won which city contracts. And that's with manual effort, or perhaps some scraping and keyword searches.

The modern version would be to write a scraper, track new posts, extract various trends, and either flag anything interesting for further review or kick it over to a reasoning model and have that model guess what's going on. But once that's a risk—once any competitor who spends a few minutes setting this up can get instantly alerted if a company is considering a new market, vertically integrating, prepping for an IPO, etc., the incentive is to make the job listings more vague. And that's not "more vague," in an absolute sense, but "more and more vague, all the time," until the jobs page basically exists to confirm publicly-available information about roughly what the people at that company do and approximately where in the world their offices are.

This is particularly acute in AI: it's an incredibly competitive space, companies have many degrees of strategic flexibility, many of the founders of big labs despise each other and probably derive equal utility from subtracting  a dollar from their competitors' net worths as they do from adding one to their own, and, yes, they are all pretty good at using AI. As the ratio of compute to headcount goes up, the signal in a job listing also rises. It tells you what kinds of intelligence and agency a company wants that aren't captured by AI agents, and where it thinks verification is going to be hard. Increasingly, talent strategy is data strategy. Sometimes, this is blatant: for example, Anthropic is hiring “conceptual reasoning fellows” explicitly to help models improve their reasoning in domains with no checkable answers, like economics, philosophy, cognitive science, etc. Other times, it’s more subtle. But in general, the more AI-centric a company is, the more an accurate list of what they're hiring for basically reproduces whatever strategic plan got presented at the last board meeting. No wonder so many people there are just a Member of the Technical Staff.

Some industries already hire this way, at least for some jobs. Consider pod shops: their central task at this point is to execute a labor market arbitrage, where they lock in the best investing talent on the best terms they can and then harvest the alpha. They're market-timers: Ken Griffin flew ex-Enron traders in to talk about what they did, and he interviewed LTCM employees after their blowup, too. One of the ways for a platform to outperform is to either identify the best talent in an emerging strategy that will be able to absorb more capital soon, or to call the cycles as different strategies go in and out of fashion. If you get the best SPAC trader to sign a two-year noncompete right as the SPAC boom is taking off, it's a captive source of alpha. Which means that actually revealing such a plan is a way of saying "I, a very smart guy, hereby encourage all of my similarly smart friends to start bidding against me for the talent I like the most." So, they tend to go easy on the job listings, to slow the information leakage.

A similar process is happening in the world of investment pitches. A classic way to write a stock-pitch newsletter is to have a few bullet points—some mix of a cheap multiple, this-is-a-real-business validation, a description of a catalyst, maybe some management action that makes the hypothesis look more plausible—and then to have the paywall kick in right before the ticker. So, you can dump those bullet points into your chatbot of choice and, generally, find exactly one company that clearly matches.

A subtler variant of this is what you might call the "Straussian Singularity": it will keep getting harder to drop esoteric hints, or to do actual dog whistles in text. It used to be a status marker to drop an apt literary reference, which fell out of fashion as a form of intra-elite jockeying once that elite didn't consist mostly, but not entirely, of people who would recognize a Horace quote in Latin. Now, that kind of thing feels pretentious and try-hard; you want your Shakespeare allusion to be easily missed, but potentially recognizable, so a reader catching it doesn't denote a foregone conclusion. But suddenly, anyone who recognizes that a reference is being made can just plug the relevant quote into their chatbot of choice and find out exactly what the reference is. In that case, once the reference is recognizable, it's hard to deny; an overall Straussian reading of some essay is going to be fuzzier (Straussians can only be a school of thought for as long as they keep arguing with each other). But we are all leaking more information in our public statements than we used to. For now, it's a question of motivation, but over time, people will start assembling broader AI-driven dragnets to infer useful facts dribbled out by various sources—maybe a tweet gets deleted months after the fact, and that, coupled with an RFP and a tweak to a footnote in a 10-Q will be the confluence of datapoints that makes a sufficiently diligent observer suddenly confident about an imminent merger, product launch, or lawsuit. If there's a positive return from this kind of thing, these privatized sigint processes will probably keep reinvesting. They'll provide baseload demand for inference through the constant process of iteratively figuring out which things are capable of being figured out, and then figuring them out.

## Elsewhere 

### Stock-Based Compensation

The employee view of stock-based compensation is that it's a part of your pay that's tied, in a noisy and approximate way, to the success of the entire company you work for, which encourages you, in a similarly approximate way, to do what's good for the firm rather than what's good for you. This is a much bigger incentive when you have points of equity rather than a sub-basis point stake in a massive business, but it still matters. But from the company's perspective, equity comp is a way to finance the business by having employees buy equity through foregone salary. The incentive alignment is a nice side effect. It turns out that you can do this after the fact, with a twist: Mukesh Ambani is asking Reliance Industries' 400k+ employees to invest in the IPO of their Jio Platforms unit, and paid their salaries early so they'd have the liquidity. Part of this is an after-the-fact attempt to give them equity comp, but it's also a way to engineer an IPO pop—and to ensure that the workforce risks being underwater on their purchase if they don't do whatever it takes to keep the stock price high. A positive externality might be that this brings more Indian households into the stock market. Right now, under 10% of the population is invested in the market, though this is changing fast.  

### Infra

The FT has an overview of the recent selloff in AI infrastructure bets. What's interesting is that there are two different dynamics at play:

1. Supply and demand will never be perfectly matched, and the labs know they've spent a lot more time short of capacity than dealing with an excess. But capital providers aren't as scaling-pilled as the labs, so they view that as a sign that the companies they're investing in will keep raising more money and rolling the dice indefinitely. 
2. Some high-profile problems, like Microsoft abandoning an Nscale datacenter project just ahead of Nscale’s IPO or Oracle's Project Jupiter, are supply-side problems: to realize their fantastic hypothetical IRRs, the neoclouds actually need four walls, a roof, racks of GPUs, power for all of them, and for every step of this to survive local politics.

So, some datacenter selloffs are bad for everyone in the space. But some of them are a relief to some companies, and a disaster to others.

### Balance Sheets

One part of the AI buildout story is that we slowly use up slack in different parts of the supply chain—we run out of new freely-available datasets, so labs have to license them or create the data themselves; fabs run at full capacity, so new fabs get built; datacenters need power, and generating and moving power has bottlenecks and backlogs of its own. A parallel process happens on the balance sheets of different financing providers; VC gets tapped out, growth equity steps in, big tech free cash flow turns negative, we find a layer of the supply chain that can borrow, etc. It would take a lot of foresight to have read the first paper on the transformer architecture and wonder if this would lead to a shortage of electrical transformers some day, and, similarly, it's a bit surprising that one constraint on AI is that it may lead to more insurance claims, and its use might be bottlenecked by insurance companies' willingness to take on big, lumpy risks. What's especially interesting about this one is that it has more of an effect on frontier capabilities than on the deployment of existing models. The big, nonlinear risks come from very smart agents doing something unpredictable and crazy; a deterministic process with a model in the loop somewhere is less risky. So to the extent that this turns into a drag on AI generally, it's more of a problem for frontier capabilities than everyday use cases. One way this can play out is that the labs themselves will need to be better-capitalized, or they'll need to find more customers who are willing to take on the liability themselves. That makes AI even more capital-intensive, but that's the natural course of things; the bigger an impact it can have, the bigger a liquidity buffer it needs.

### An Ownership Society

One of the policy drivers behind how wonky the real estate market got in the 2000s was the Republican party's vulgar Marxist analysis of voting: homeowners tend to vote for Republicans, so if we make it easier to buy a home, we make it easier to become a Republican! Plenty of politician try to distribute money in a way that gets them more votes, but this version turned out to be a pretty inefficient way to turn taxpayer dollars into votes (especially since these policies really ended up borrowing votes from 2008, at an exorbitant interest rate). 

But some of that just came down to implementation: encouraging homeownership through borrowing creates some systemic risk, and homes are not an ideal investment vehicle—owning one makes it harder to move to a new city, for example, so it can create a city-level credit cycle. But there are more abstract and mobile kinds of capital, like equities, and they enable a different way to engineer an ownership society. Trump Accounts have provided a mechanism for various wealthy people to airdrop the asset of their choice; this morning, Elon Musk said he'd give every child in America a share of SpaceX, following the Dells, who donated a bit more in cash. It's not clear that the average person will feel that a few hundred dollars compounding away for them makes them part of a different class, but it is still a form of redistribution, and, unlike some of the other ways someone might make first contact with financial markets, Trump Accounts don't encourage rapid trading and risk-seeking behavior. They make the market pretty boring except for the possibility of eventually pulling some money out—but for most people, a shift in that direction is probably a good thing.

### The Sonic Boom of Trust

The Diff has mentioned a few times that AI makes many information-wrangling problems more tractable, and that includes going back and catching someone in a lie. But sometimes it means the opposite: Anthropic was able to recover the original seed value for the ECDSA encryption standard, and it's—innocuous! They're just the output of a standard random number generator, with Unix timestamps as the seeds. So, in retrospect, the NSA of the 90s was slightly more trustworthy than we gave them credit for. Since many of the AI-enabled discoveries of this kind are the opposite—e.g. using Pangram to find out that someone didn't write their own work—it's nice to see some symmetry here.