• Ruth Michaelson in BusinessWeek writes about bank robbers in Lebanon who are robbing banks to get access to their own funds. It's a depressing story, and the background is a bit of a throwback: Lebanon was able to achieve economic growth by pegging its currency to the US dollar and then allowing credit expansion at home. In the short term, especially in a credit-constrained economy, that's actually a winning strategy: the economy gets more financial firepower to support the currency peg. But if it goes on long enough, the banking system ends up undercapitalized, dollars get scarce, and the financial system collapses.
• Alex Tabarrok has a good paper on Baumol's cost disease and its connection to the "Linder Theorem," the observation that the richer people get, the busier they seem to be. This is noticeable in big cities; of course people in New York walk fast, since there are so many places to go! It's a generative set of ideas, helping to explain everything from US healthcare costs to affordable haircuts in the developing world to video on demand. And it's a great example of a pedagogic observation about economics: even economics students can struggle to explain the concept of opportunity cost—but the average person definitely acts as if opportunity cost is real.
• Pradyumna Prasad has a good piece on how the Economic Freedom Index ignores some important factors, most notably the government's ownership of the economy and land-use regulations, giving misleading numbers. There are two parts to this: the land use point is absolutely correct; limiting housing supply in San Francisco, Hong Kong, and London is a government policy that reduces economic freedom by raising prices. On government ownership of land, and of stakes in businesses, the answer is trickier. In one sense, equity ownership operates equivalently to corporate taxes: either way, in the long run, the government collects some share of a company's profits. But marking the value of future tax receipts to market, which is effectively what happens when you replace a 20% corporate income tax with a 20% stake in the business, does have value: it provides a real-time view of the market's estimated impact from policies. It also makes some things more commensurable: it's hard to price the importance of domestic control of some strategic company, but putting a literal price on it, where when the government cedes control of some company it gets a cash windfall, helps to make the calculation easier.
• William Broad in the NYT has a surreal but fun article on how annoying the number three is. In many systems, one participant is trivial to model, two are pretty straightforward, and three—whether it's three stars in a solar system, three magnets affecting an object, or three superpowers fighting for dominance—have nonlinear effects. (What about four or more? At some point, you get to graduate to being able to look at statistical averages where one-on-one interactions mostly cancel each other out.) It's adjacent to the phenomenon that for most phenomena, there are roughly zero, roughly one, or pretty much infinitely many instances; there are other numbers, sure, but they get tricky fast.
• Tyler Cowen interviews Reid Hoffman, mostly about AI. Hoffman talks about AI regulations and the idea that every AI should have some legal person or company associated with it, and Cowen makes some good points about how hard that is to implement in practice. (Will every cloud company verify that every process run on their machines is not, by some definition, AI, and that if it is AI it has a responsible owner?) There are also good thoughts on AI in education—biting the bullet and having ChatGPT generate mediocre essays that students then edit into excellence. And the best point from the piece, about both the limits and complementarity of AI tools: LLMs are much better at answering questions than at asking them. Which makes sense; an answer fits the "predict the most likely next token" model very well, whereas a good question starts with a model of the world and awareness that this model is incomplete. One way to frame this: AI can answer questions, but so far can only simulate asking them.
• In this week's Capital Gains, we look into how one should read an S-1. (A confession here: yesterday's Diff post about an AI/makeup IPO ($) looks like it's following the advice here, by finding a case where the company buries an important disclosure about their business model in the "Risks" section while portraying it differently elsewhere. But what actually happened in this case was that I asked my wife about the company's products and got a short course in what people find frustrating about DTC subscription economics.)

Books

• American Republics: This book covers American history from 1783 to 1850 (part of a longer series). The reading of this period that the book argues for is a twist on more purely slavery-centric narratives—the driving historical force in the book is a longstanding effort to avoid dealing with the question of slavery. American expansion ended up being a sort of ponzi scheme where every newly-claimed territory a) gave the pro- or anti-slavery side more influence in elections, and b) provided a safety valve where people dissatisfied with life in a more settled part of the country could move somewhere cheaper with more opportunities. This was a meta-instability on top of a system that was already fairly chaotic. It's a wonder it ever worked.
  
  Via Scholars Stage.

Open Thread

• Drop in any links or comments of interest to Diff readers.
• Some companies do some of their technical hiring by posting puzzles on their site and interviewing people who provide a good solution. But they tend to deprecate and eventually remove this as they grow. (Google, for example, once bought a billboard inviting people to go to [first ten-digit prime found in the digits of e] dot com in order to apply for a job.) Are there any companies out there that got a disproportionate share of their employees from this kind of source?

Diff Jobs

Companies in the Diff network are actively looking for talent. A sampling of current open roles:

• A well funded seed stage startup founded by former SpaceX engineers is building software tools for hardware engineering. They're looking for a full stack engineer interested in developing highly scalable mission-critical tools for satellites, rockets, and other complex machines. (Los Angeles)
• A vertically integrated PE-backed company applying a rigorous investment/operations approach to a high-growth industry is looking for an analyst who has banking experience. (Little Rock, AR—no remote, but relocation assistance is possible)
• A fintech startup that gives companies with complicated financials a single source of truth for managing their cash flows and understanding their unit economics is looking for a founding engineer with JS, Typescript, Node.js, and React experience. (Bay Area, Hybrid)
• A new health startup that gives customers affordable access to preventative care and lifestyle interventions seeks a founding engineer. 7+ years of JavaScript experience preferred (TypeScript is ideal), and payments experience is a plus. A great opportunity for anyone excited to make healthcare better by treating problems cost-effectively before they're catastrophic. (US, remote; Austin preferred)
• A firm using machine learning to customize investments is looking for a data engineer. (NYC)

Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.

If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.

The Economics of Engineering Blogs

A company is valuable if it does something that customers value, and that other companies can't do. A consequence of this is that companies have a vested interest in keeping secret anything that would help their competitors copy them better or would help their customers solve the same problem in-house. So oil and gas companies usually don't go around telling everyone about which leases are undervalued, hardware companies try keep new devices under wraps, and traders don't talk about trades they're making (but do, of course, talk about trades they've already made from time to time—if you’ve made a bet that the consensus on something is wrong, popularizing your thesis is the fastest way to make it right), etc.

And then there are software companies: a Google engineer is perfectly able to read Meta telling them how to scale a global network of datacenters, Netflix will happily share some tips YouTube can use to automate the detection of scene breaks in shows, Shopify wants you to know how to improve Ruby's garbage collection, GitHub will teach GitLab how to improve natural language searches of source code, Spotify will give a boost to Apple Music's efforts to customize a playlist, and there's nothing stopping someone at Indeed from perusing LinkedIn's post on how to deliver code faster.

It's a big enough category of content that there's even an aggregator devoted to engineering blogs, a surprise since it’s counterintuitive that these exist at all—tech companies are constantly at each other's throats. In some categories, they compete head to head, and in others, they're in adjacent layers of a supply chain and thus have a strong incentive to commoditize one another ($, Diff). Many of them offer products that could, at least in principle, be replaced with some homegrown solution. And here they are, sharing all kinds of helpful writeups about their successes and failures, without bothering to geofence their competitors out.

Why?

There are theories of varying levels of cynicism here. Two closely-related ones are the intimidation model and the wild goose chase approach. Why does Meta want you to know how to optimize datacenters? So you come away impressed with the cost advantage they've acquired, and worried about how much better they'll be by the time you catch up. You can read any “How we succeeded” post as a warning not to even try, at least in that domain. The wild goose chase model suggests that engineering blogs focus on projects that are much harder than they seem to be, and that require extensive upfront work just to demonstrate how much time the finished product will take. In this model, the result of a fascinating writeup on a cool project is wasting their competitor’s time. There are precedents for this; allegedly, the multi-armed bandit problem was so time-consuming to Allied researchers in the Second World War that they looked into ways to get the Germans to try to solve it specifically to waste their time.

But these both point to a better model: engineering blogs focus on problems where the solution is a necessary but not sufficient part of what they do. And, ideally, they focus on problems that are complementary to scale that only the publisher of that post has. Take Netflix's recommendation, or pretty much any case where a huge company talks about applying machine learning at scale: the safest time to do this is when the scale of the problem is big enough that smaller companies wouldn't get similar results.^[1]

That would explain why publishing doesn't have much downside. But what about the upside? There are a few cases here:

• It's good for recruiting: people want to work on interesting problems, and sharing the results is one way to attract them. (In a way, this is the entire model of academia: a published paper is partly an invitation to build on or refute its results.)
• It's a way to get feedback, which could be a subset of recruiting, but not necessarily. Most of the time, there's a good answer to "Why didn't you just do X?" but occasionally there is some X that was actually worth doing. (This can be especially interesting when there's an obscure theoretical result that has relevance to the practical implementation. Especially in ML/AI, the pace of publications is too much to keep up with, and the lead time between starting and finishing a project can mean that the state of the art changes over the course of the implementation.)
• It helps with retention: there's some amount of labor alienation in being one of thousands of people making incremental contributions to some massive system. There are engineers at big companies who have, single-handedly, created human lifetimes worth of free time for people just by reducing the latency of some app.^[2] The numbers are similarly staggering when you consider monetary impact; spend enough time among people who work at big companies and you will meet individuals who have shipped features or even tweaks that can be rigorously shown to have produced hundreds of millions of dollars in value. Giving them an opportunity to take some credit for this in a blog post—to be able to tell friends and family members who use the service they contribute to that they helped to make it what it is—gives people another reason to keep doing more of the same.
• Some engineering blog posts help demystify the product, or allay conspiracy theories. Why does Meta walk through the ranking algorithm for the news feed? To make it slightly harder to claim that somewhere in the code there's a line like if source == russian_propaganda_mill then pin_to_top_of_feed. And even there, the point isn't to stop people from saying that, but to make those people seem less technically sophisticated than the people who don't think it's happening.^[3]
• Earlier, we noted that traders don't publicize their ideas while they're putting on a trade, but will sometimes share them later. This doesn't apply to most engineering blogs, but it does work in some cases: publishing good documentation about impressive projects done using the company's chosen tech stack is a way to promote it as a standard and own part of that standard ($, Diff). It's bad for Shopify if someone else writes a giant e-commerce suite in Ruby, but it's overall good for them if more companies are using Ruby for huge projects, since it deepens the hiring pool. (Ocaml would probably be a much more obscure language without Jane Street's promotion (plus, they went even further than blogging and wrote a book, too). So there are a few cases of hyperstition going on, though it's less about blogging something into existence and more about using the blog to set a more favorable equilibrium.

Using an engineering blog as a recruiting tool essentially forces the company to raise its talent metabolism: it's easier to attract good people, but outside recruiters can see who published what, and the more impressive the project is the more likely its participants are to get attention from other employers.

But for historical reasons, the marginal cost of this is lower in software than in other industries. The biggest cluster of software companies globally is in and around Silicon Valley, which means it's subject to California's labor laws—which include a notorious reluctance to enforce noncompete agreements. It's a small world, and there's a whole cohort of people whose job is to simultaneously be well-informed, be well-connected, and to catalyze people either leaving companies or starting them. So tech's talent metabolism has always been running hot, even before the software industry as such existed. As Tom Wolfe put it, writing about the early days of the semiconductor business: "Every year there was some place, the Wagon Wheel, Chez Yvonne, Rickey's, the Roundhouse, where members of this esoteric fraternity, the young men and women of the semiconductor industry, would head after work to have a drink and gossip and brag and trade war stories about phase jitters, phantom circuits, bubble memories, pulse trains, bounceless contacts, burst modes, leapfrog tests, p-n junctions, sleeping-sickness modes, slow-death episodes, RAMs, NAKs, MOSes, PCMs, PROMs, PROM blowers, PROM burners, PROM blasters, and teramagnitudes, meaning multiples of a million millions."

It's a lucky situation. Industries can thrive by keeping secrets, but if they're at least partly competing on openness, overall progress happens a lot faster. More industries are slowly moving in that direction; software had a head start because the barriers to entry are so low and because so much of the tacit knowledge can be passed down in text form. Video has made it easier for other fields to share information the same way. It won't go as far in other fields, both because of the path dependence behind software's openness and because nearly-zero marginal costs mean that when a software product gets commoditized, the price doesn't drop by 10% or 20% or 50%, but goes straight to zero and stays there forever. But in tech, it's not an advantage to practice this kind of openness; it's a competitive necessity. Which means that in other fields, being the first to do this, and to do it well, is a meaningful advantage.

Thanks to Gergely Orosz of Pragmatic Engineer for the tweet that inspired this.

Disclosure: Long META, MSFT.

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Elsewhere

Art Exchange

A company called Artex is trying to create an exchange for equity in paintings, starting with a $55m offering for shares of a portrait by Francis Bacon ($, WSJ). Art is a strange asset class. In one sense, it's an inflation hedge, since the supply is finite. On the other hand, it's a sort of supercharged bet on market beta, since historical peaks of art prices have coincided not just with market peaks but with cases where newly-rich people made their money very differently from whoever came before them, and want to demonstrate that they're still part of the establishment. (This works best for art prices when the establishment, too, can afford to make a bid.) J.P. Morgan collected art and books in part because he didn't have an aristocratic lineage to fall back on, and was able to do so because the people with the pedigree and the art collections were being left behind economically by increasingly wealthy Americans. Something similar happened to Americans, briefly, in the late 1980s; the record amount paid for a work of art tripled in 1987 when a Japanese insurance company bought Van Gogh's Vase with Fifteen Sunflowers for ~$40m. Art investments are hard to underwrite because it's unclear what will stay relevant—Sunflowers, for example, may have been painted by Emile Schuffenecker instead. And art has a carrying cost; the paintings have to be preserved, and shares aren't worth much if the painting gets stolen. (Unless, of course, it's 2021 and your shares are nonfungible tokens.)

Fraud

A few weeks after job training company Bitwise shut down amid accusations of fraud, events company IRL has wound down, saying that 19m of its 20m users were fake ($, The Information). It's extremely hard to bootstrap a user-generated content company without some amount of inorganic posting, whether that's from the founders using sockpuppet accounts (the Reddit strategy), actual bots (a common dating site scam), or even gray areas like paying users to post (Yelp was able to cultivate more content than users organically wanted to produce through its Yelp Elite parties—so the users were real, but they had a cost to acquire that would have to drop for the model to work long-term). These are all on the same continuum, and while all of them are in some sense unsustainable, they still make it possible for a site to get off the ground and reach a point where users do the work without being prodded by gifts or fake users. So the real gap between fraudulent companies and social media companies with an effective growth model is whether you can plot fake engagement against organic engagement and find a line of best fit that points to the fake engagement eventually going to zero.

Airlines

Boeing and Airbus have received record-breaking orders and can't make planes nearly fast enough to satisfy demand ($, WSJ), which has extended order backlogs into the 2030s. The pandemic was a massive demand shock, but also a bit of a reset that led some airlines to accelerate the retirement of planes. Naturally, once things returned to semi-normal, this caused a squeeze. It's generally harder to model supply than demand, because some of the factors behind supply seem unstoppable and others are intrinsically hard to predict. The supply side can be trickier, but it gets easier to assess when demand is backlogs are long. The market is more or less on board with the view that this demand swing will reverse: Boeing and Airbus shares naturally tanked right after the pandemic started, but, unlike many other companies, they're still trading below where they were in early 2020.

The Long Tail

Modern Retail has a quick profile of influencer marketing startup Kale, which tries to identify social media users who are already posting about brands and reward them for this. There's a porous boundary between organic marketing from unprompted customer endorsements and paid marketing that's part of a formal campaign. And one thing that determines the boundary is the transaction costs, both for identifying potential product endorsers and for encouraging them to share what they like about the product. In a sense, this is a productized version of the Etsy seller practice of giving customers a handwritten note asking them for a review. But as happens in many other cases, a linear decline in the cost to reach people can lead to an exponential increase in the size of the potential audience.

Chinese IPOs

Swiss chemicals company Syngenta was acquired by ChemChina in 2017, and is now being taken public in Shanghai, but American banks increasingly doubt that they'll be able to participate in the offering at all ($, FT). China's government has been able to encourage a large equity market that also functions as a tool to direct capital towards state goals—by promising large subsidies to strategic industries but not picking the winners all at once, for example, they can use equity markets as a proxy for which firms in their chosen category are likely to be the eventual winners. One ingredient of that is having a domestic investment banking industry that can handle big underwritings. In this case, the company was public before, so already evidence of investor demand, meaning it's a good opportunity for the government to give domestic underwriters experience with larger deals.

Diff Jobs

Companies in the Diff network are actively looking for talent. A sampling of current open roles:

• A proprietary trading firm is seeking systematic-oriented traders with ML experience—ideally someone who has displayed excellence in DS and ML, like a Kaggle Master. (Montreal)
• A company building tools to enable zero-knowledge proofs is looking for multiple roles, including a full stack engineer. (Remote)
• A company building ML-powered tools to accelerate developer productivity is looking for a mathematician. (Washington DC area)
• A startup building a new financial market within a multi-trillion dollar asset class is looking for a senior ML engineer, especially someone interested in using LLMs to make unstructured data more tractable. (US, remote.)
• A hedge fund is looking for an experienced alternative data analyst who can help incorporate novel datasets into systematic strategies (NYC).

Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.

If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.

• Amanda Mull in The Atlantic on the rise of vape shops. There's a whole category of businesses that are capital-light, labor-intensive, and have variable success. They're a great proving ground for anyone whose credentials don't match their abilities. These enterprises can be dubious, but they're also a good way to keep certain parts of the economy dynamic. As the article notes, vape shops tend not to sell cigarettes, so they're partly a way to accelerate the transition away from combustion and towards safer methods of nicotine consumption. The vape shops aren't doing this out of public-spiritedness, any more than the convenience stores are insisting on combustibles because they're big fans of emphysema. Different models have different local maxima.
• Ben Carlson tracks the long evolution of financial advice, and how the market has changed over time. In 1950 it was hard to get financial information, but the market was almost entirely retail investors. Today, retail investors have access to much better data than ever before—but the relative information advantage still lies with institutions.
• Dietrich Vollrath breaks down the sources of slower economic growth in recent decades. It's a high-level overview that looks at a few features of human capital—labor force participation, hours worked, education, age—and measures their impact on the change in growth rates. There are some surprises here: in Germany, for example, if labor force participation and hours worked hadn't changed from their late-20th-century trendline, the country would have gotten poorer over the last twenty years.
• Maria Streshinsky interviews Christopher Nolan in Wired. Topics include existential risk, how a traditional filmmaker thinks about AI, and nihilism. One thing Nolan has adapted from AI already is the warning-as-sales-pitch: "Some people leave the movie absolutely devastated. They can't speak."
• Madoc Cairns on Polish philosopher Leszek Kolakowski, who started his intellectual career as a Marxist atheist and died as an anti-Marxist Catholic. One interesting detail is that it's a live example of Straussianism: he was spied on by Poland's secret police, but most of the people he wanted to converse with were fellow intellectuals, so he could afford to be obscure in his prose. "His editor, Zbigniew Mentzel, once said he planned a novel: after a protest in the spring of 1968, three students were locked in the same cell. All attributed their participation to a lecture by Kolakowski. All three disagreed on what the lecture actually meant."
• In this week's Capital Gains we look at the "Minsky Moment"—sometimes, the financial system stops reflecting fundamentals and starts creating them, but that can't last forever and the end is unpleasant. And "financial systems" are just a subset of the more general category of trustworthy promises, so you can have a Minsky Moment in a community or even a civilization.

Books

• Outsourcing Empire: How Company-States Made the Modern World. OK, many historians like to argue that their particular specialty "made the modern world." (A quick search reveals that the Modern World was single handedly Made by: Europe, Japan, "Six Innovations," Play (by the same author as the Six Innovations book), the car, English-speaking people, Muslims, Christianity, Protestants, Meritocracy (briefly reviewed in The Diff, and of course Freemasons.) But this book makes a solid claim. Company-States like the British and Dutch East India Companies did, in fact, conquer large territories while also creating the only institutions of that time period that look remotely like the modern corporation. They also opened new frontiers in diplomacy: historically, many national leaders insisted on being treated as an absolute ruler of all of humanity. It was a bit awkward for a diplomat representing one self-styled universal monarch to ritually agree to some other self-styled universal monarch's claim of world domination. But it was not at all difficult for businesspeople to ritually abase themselves to the Badshah, Sultan, Emperor, etc. (Would someone really do that? Say plainly untrue things in a humiliation ritual in order to do business with a large country in Asia?) The book is also a reminder that even successful institutions are creatures of a particular set of circumstances. There was a time when the best way to ensure profits was for big trading companies to maintain navies and standing armies, and to defend their market share by waterboarding or decapitating employees of competitors. But eventually a different model prevailed.

Open Thread

• Drop in any links or comments of interest to Diff readers.
• What are some political/economic arrangements that are either due for a comeback or can be expected to get much more common?

Reader Feedback

Søren Fryland Møller had some thoughts on Thursday’s post on hurdle rates. Some highlights:

Shareholders (unless very concentrated ownership) only cares about the systematic risk not the idiosyncratic etc etc.

For management, idiosyncratic risk matters a lot, and getting a single long term investment wrong matters a lot more to them than the average shareholders.

Yes, idiosyncratic risk should be in the cash flow forecast and only systemic risk in the CoC and hurdle rate, etc. - but it's rational for managers to operate with hurdle rates that are much higher than the shareholders' estimate of the company's CoC given this difference in risk exposure. It's "cleaner" and easier to use a high hurdle than a complicated probability weighted cash flow forecast.

This is true, but also seems like an incentive misalignment. Essentially, managers are managing their risk in order to maximize company-level risk-adjusted returns, but shareholders in the aggregate probably want them to maximize portfolio returns. Which raises some interesting possibilities: does this explain some of private equity’s historical excess returns? They might incentivize managers to take risk in a way that benefits the portfolio instead of one company.

A Word From Our Sponsors

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Diff Jobs

Companies in the Diff network are actively looking for talent. A sampling of current open roles:

• A VC backed company reimagining retirement wealth and building a 401k alternative is looking for a product manager with fintech experience. (NYC)
• A vertically integrated PE-backed cannabis company is looking for a production analytics manager to optimally allocate plant biomass. Excel wizards preferred. (Little Rock, AR—no remote, but relocation assistance is possible)
• A well funded seed stage startup founded by former SpaceX engineers is building software tools for hardware engineering. They're looking for a data engineer interested in developing highly scalable mission-critical tools for satellites, rockets, and other complex machines. (Los Angeles)
• A fintech startup that lets investors trade any theme as if there were an ETF for it is looking for a senior backend engineer. (NYC)
• A profitable AI startup is looking for ML engineers to help build new services to help small companies accelerate their growth. (SF)

Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.

If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.

Reddit and Platform Politics

For the last week, large swathes of Reddit have gone dark in protest of Reddit's plan to increase the price of their API. The reaction comes after price hikes rendered some third-party apps economically untenable, and irritated Reddit's power users enough that they banded together to temporarily cripple the site. Most of the subreddits that went dark have come back online (as of this writing, this tracker shows 3,409 of the 8,829 subreddits that participated in the strike are still restricted in some way, though it's skewed to smaller subreddits; r/funny, with almost 50m readers, is back open, while the list of still-closed ones are for things like UK-based mechanical keyboard fans, or people who brew moonshine). This is partly a story about platform economics, but it's also about the social contract and the evolution of legitimacy. It's a classic example of centralizing a decentralized system: "We tried to switch back to running things top-down, and that's when everything went sideways."

Reddit, like most platform companies, has found it optimal to offer a mostly-free service with a handful of paid services. The business of building a status hierarchy where people compete by sharing or moderating content, monetized through ads, has worked well whether the fundamental unit of content is a link (as on Reddit), a short video (YouTube, TikTok), or 140 characters (Twitter). It also works as a vertical-specific model, or a collection thereof, as in the cases of Stack Exchange, Fandom, and SumZero. Most people don't contribute much, but for a subset of them, it pays, whether through status, through indirect economic benefits, or just because it's fun.

Which immediately brings up a question of who really owns the site. There are a few different possibilities here:

1. As a legal matter, Reddit is owned by its shareholders and managed by its managers. If they want to change the terms of service to increase the cost of API calls, or to replace moderators with people who will reopen subreddits, they're within their rights.
2. In some sense, though, Reddit Inc. is a sort of trustee for the collective asset built by the people who contribute to the community—posters and moderators. They're what make the site worthwhile. The underlying technology took money and effort to create, but since you can download the site's source code circa 2017 for free it’s probably not the true source of value.
3. The broadest conception of Reddit is close to a public utility whose main users, and thus biggest interest group, consists of passive lurkers who read the site but never comment. The trick of appending "reddit" to a search query to get answers from real people is so ubiquitous that Google turned it into a feature. These users, in the aggregate, fund the site because they're a large share of ad pageviews. They don't impose any meaningful cost burden other than bandwidth and servers, so they're a profitable demographic.

When the site started, groups 1 and 2 were in control, because they were the same people: Reddit's founders created sockpuppet accounts to post stories so the site wouldn't look empty. Over time, the site grew, and more authority got delegated to unpaid moderators, who create and enforce rules for the subreddits they manage. And those moderators have been very beneficial to Reddit:

• There's the obvious first-order benefit that Reddit doesn't have to pay its own moderators to enforce the rules.
• A secondary benefit here is that when there's a controversial moderation decision, it comes from some individual running their own mini community, not Reddit as a whole. Moderation always involves edge cases, and edge cases are a no-win situation for big platforms: if they're too strict, users complain that they're arbitrary; too lax, and the content the site allows becomes newsworthy. (And even if they try to avoid both issues by enumerating every controversial answer in advance, the rules get leaked and every one of them inspires hypothetical edge cases.)
• The fact that every community has different rules means that moderators can create niche groups that are perfectly designed around one kind of content and one kind of discussion. (One funny result of this is that when some communities reach critical mass, spinoff communities can form based on hair-splitting disagreement with the mods.)

On the other hand, the moderators can also make thousands of high-traffic communities inaccessible to users for a few days. There are always tradeoffs.

For context, this is not the first controversy between Reddit's users and the company; a discussion-oriented site naturally selects for people who like to argue, and a site built around virality is also a good venue for making a protest go viral.^[1] In fact, during the current CEO Steve Huffman's original time as CEO, Reddit's then-parent company took down a front-page post at the behest of a big advertiser, which seems to have precipitated Huffman's departure from Reddit. So Reddit's CEO is used to dealing with the community, and knows that the general bet is that even if management makes a mistake, the site will keep going.

Huffman has been on a media tour recently to give the company's side of the dispute. One interesting line from one of these interviews: "If you’re a politician or a business owner, you are accountable to your constituents. So a politician needs to be elected, and a business owner can be fired by its shareholders... And I think, on Reddit, the analogy is closer to the landed gentry: The people who get there first get to stay there and pass it down to their descendants, and that is not democratic."

This is apt! The system is somewhat feudal, not in the mildly pejorative sense in which a normal person might dislike it, but in the sense that an anarchist might like it. It’s a complex system of reciprocal social obligations, some unstated, that an outsider simply can’t expect to fully understand.

Many systems have a tension: between decentralization that takes advantage of local knowledge and that converts the social capital of a community into a monetizable asset for the company, and the centralization required to get anything done. And the usual way centralization works, in any system that weights popular opinion, is that the centralizer gets legitimacy by saying they're operating on behalf of the average person. Caesar had his populares, Nixon a Silent Majority, and Perón the descamisados. Operating with this kind of support is democratic in the sense that it turns popularity into legitimacy, though less democratic in the sense that it compresses "political" participation into the binary decision to either vote for the incumbent by sticking around or to leave.

And there's also the meta question about the granularity of freedom: if moderators can decide what shows up on their subreddits, then subreddit members can't. In an interview with The Verge, the interviewer points out that many of the most popular posts on Reddit at the time were complaints about the API policies—and Huffman responded by noting that comments were turned off on those posts: "If there were comments on there, I bet I can tell you what those comments would say. They would say “knock this off, it’s annoying.” Because if you go to the other posts where comments are enabled, that’s what people are saying." (As of this writing, the front page has one post, from the developer of the Apollo app, supporting the blackout. And there's one post complaining about complaints about a subreddit's protest—the imperative to riff and remix content for karma is irrepressible.)

Reddit is making a business decision, here. They say in the Verge interview above that the infrastructure cost of supporting third-party apps is $10m, and that doesn't include the apps' share of their operating expenses or the opportunity cost of replacing ad-supported pageviews with ad-free ones.^[2]

Ultimately, Reddit will do what it needs to do to make the business sustainable. Its immediate revenue needs will have to be balanced against what the community wants, but that also requires knowing what the community is. If you look at what community moderators are doing, you'll get a wildly distorted view of what the median user wants. Even if you ask users, the ones who are likely to answer and likely to have strong views will be a distinct group. (As a case study, this post highlights all the sites moving off Reddit—and, in the footnotes, laments the inconvenient closure of a subreddit that could have provided some helpful research.) Reddit has 400m+ monthly active users. Are there 400 million people in the world with strong opinions on AI pricing? Are there even 4 million? The protestors are correct that Reddit is killing businesses that were built on its platform, and Reddit is also correct that these businesses were weakening its ability to monetize, which could eventually threaten the product itself.

And this kind of discussion extends far beyond Reddit. The big downside to having a consumer brand is that the product ends up being one that everyone has an opinion on; the downside to outsourcing important work to unpaid moderators is that they have opinions about what direction the site goes in and what decisions the business behind it makes. And plenty of other companies are in the same position, where much of the value is created by third parties who use the platform but don't have input into how it's run. Strategically, it's better to control anything that's mission-critical for the business, but that doesn't fully solve problems. As Reddit's situation demonstrates right now, it just pits companies against a whole new category of problem.

Disclosure: Long META.

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Elsewhere

The Boomer Market

A good way to start explaining the existence of the financial system is to point to consumption smoothing: everyone's earnings start at zero, then move up gradually, typically peak in their fifties, and then generally decline to zero. Unless the plan is to live a monastic lifestyle in your early twenties and really get into partying in a big way thirty years later, the usual way to address this is to borrow money early on, especially for housing, and then accumulate net savings that can provide a supply of money to match the next generation's demand for debt. This model has been directionally true for a long time, but there are quirks. The baby boomer generation entered their peak savings years at a time when rates were high and assets were, as a consequence, cheap. As a result, they've been holding equities longer than expected ($, WSJ). For decades, people have speculated (including this author) about what would happen when the boomers started selling equities and homes and moving into other asset classes. Surprisingly, they haven't really done this; the ones who aren't rich don't have much stock to sell, and the ones who are rich don't see the need.

Network Effects

The site where gamers want to stream videos for money is the one with the biggest audience; the site with the biggest audience is the one that attracts the most popular gamers. And people like watching gaming streams. This is the short bull case for Twitch, which dominates video game streaming. But as with other network effects, once it starts unwinding the math works in the opposite direction. A competing site, Kick, has signed a deal with streamer xQc worth up to $100m. Part of the point of this is, of course, to (very expensively) buy an audience. But it's also a way to set up a nice tournament dynamic: newer streamers may choose the platform with the largest headline number in its deals, not the largest immediate audience—which, of course, puts that other platform on a steeper growth trajectory. Of course, the most likely outcome is that the network effect doesn't kick in; the last such deal was with a platform that shut down a year later. But since the odds of failure for a new streaming platform are already high, the question is what they can do that increases the variance as much as possible.

Direct to Retail

The WSJ highlights the trend of companies avoiding direct-to-consumer models and trying to market their product through retailers ($). These things move in cycles: the DTC bet was that companies could understand their customers better with a direct relationship, and could avoid giving up margin to a retailer. But that also required expensive marketing campaigns and distribution; it's harder to ship individual products to end consumers than to ship pallets to warehouses. And retailers were also aware that e-commerce was eroding their foot traffic, and have adapted by changing their offerings. As it turns out, there is some useful specialization in the retail business, and while it's not strictly a bundle-based enterprise, a retailer can end up providing a better selection to customers and wider distribution to suppliers.

Strategic Investing

Japan Post Bank, which has 24,000 branches and ~$1.6tr in assets, is planning to invest directly in startups ($, Nikkei). A big bank has a slight advantage here in that they can track deposit flows across all of their customers, so as long as either depositors are spending money directly with the startups in question or the startups themselves are depositors, a list of the fastest-growing companies in Japan is one SQL query away. Offsetting this advantage is the fact that banks tend to move slowly, and can't put too much of their balance sheet into risky assets (fine in this case; the investment is less than half a percent of assets, and about a tenth of equity—and the deposit base is very sticky). At some point, a majority government-owned bank won't be the ideal vehicle for making this kind of investment since it won't have the right operating cadence, but in that scenario the bank will have a nice markup on previous investments, and will also be tied to the growth of a more dynamic economy.

ByteDance and AI

TikTok parent ByteDance has ordered $1bn worth of Nvidia GPUs so far this year (the linked article says this exceeds all of China's orders for 2022, but Nvidia's 10-K says that China and Hong Kong were responsible for $5.8bn of revenue last year—still a big deal). The first obvious use of AI for a user-generated content platform is to continue improving content recommendations, and an immediate follow-up to this is to improve usability (automatic captions, machine translation, video effects). But in the long run, the better recommendations get the more likely it is that the best recommendation is something that doesn't exist yet, but could.

Diff Jobs

Companies in the Diff network are actively looking for talent. A sampling of current open roles:

• Ready to leave banking or consulting and jump straight into PE? A vertically integrated PE-backed cannabis company is looking for a data analyst with visualization experience. Excel wizards encouraged to reach out. (Little Rock, AR—no remote, but relocation assistance is possible)
• A fintech startup that gives companies with complicated financials a single source of truth for managing their cash flows and understanding their unit economics is looking for a founding engineer with JS, Typescript, Node.js, and React experience. (Bay Area, Hybrid)
• A company that helps investors use alternative data to make better decisions is looking for early-career data scientists and business analysts. (Remote)
• A company building ML-powered tools to accelerate developer productivity is looking for software engineers. (Washington DC area)
• A profitable AI startup is looking for a product designer for its new services that help small companies accelerate their growth. (SF)

Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.

If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.

• A fun time capsule: a 2005 NYT column about the alumni network of Drexel Burnham Lambert, written a decade and a half after Drexel's collapse. Business mafias are rare because they require two things: a concentration of talent, and then some catalyst for all the talent leaving at the same time (without the bad blood that would make them reluctant to do business with one another). In Drexel's case, the catalyst was an indictment that simultaneously killed the firm and helped highlight just how much money the people there were making.
  Via Matt Levine.
• Jon Askonas in Mere Orthodoxy on how conservatives should think about technology. This is especially worth reading if you do not consider yourself conservative—technological changes disrupt social equilibria, so if you're broadly happy with social norms today, and technology keeps improving, then you’ll inevitably become very worried about The Way Things Are Going Nowadays. The essay also has some good thoughts on the question of preserving versus recreating traditions; some things that screen as "traditional" are actually quite new (the viral example of this is the claim that Chicken Tikka Masala was invented in Britain in the 1960s; another fun one is that the fez, banned by Atatürk in 1925, had only been traditional Turkish attire since the reign of Mahmud II, roughly 100 years before).
• Jay Caspian Kang writes about the career arc of Nate Silver in The New Yorker. There's a wonderful irony to Silver's career: he's been successful at thinking rigorously about probability. (If you viscerally disagree with that, please include a link to a prediction calibration graph like this one! Thanks!). But he got famous for an improbably successful record in 2012, when he successfully called every state. His audience wanted certainty, particularly certainty about what they hoped would happen, but Silver doesn't sell what they're looking to buy.
• Niels Joachim Gormsen and Kilian Huber have an important paper on corporate discount rates. Financial theory assumes that companies invest in projects whose returns exceed their cost of capital. This is value-creating, even when both rates are low: if a company can invest for a 6% return, but its cost of capital is 5%, then raising money and investing it makes the business more valuable. The paper looks at companies' statements from conference calls (they manually reviewed 74,000 paragraphs of company communications) and finds that companies will often explicitly call out the fact that their threshold for investments is higher than their cost of capital, and that these required rates of return are insensitive to changes in interest rates: a one-point rate drop in the cost of capital leads to a 0.3 point drop in discount rate. And the result is that companies invest less than financial theory says they should—which leads to slower growth, and thus lower rates, further compounding the problem! (It would be politically untenable to do this, but one solution would be to replace or enhance changes to discount rates with subsidies for investment—or, more palatably, with variable surtaxes on dividends, buybacks, and acquisitions.) Of course, any claim that companies focus too much on buybacks and not enough on reinvesting in their business is equivalent to a claim that the problem with big business today is that it isn't nearly big enough. Tradeoffs abound, as always.
  Via Marginal Revolution.
• Cormac McCarthy (RIP) writes about "Kekulé Problem," or why we sometimes know things subconsciously and then figure them out metaphorically. It's a piece that happens to be relevant today not just because of McCarthy's death but because of his insight into where thinking happens: "Language can be used to sum up some point at which one has arrived—a sort of milepost—so as to gain a fresh starting point. But if you believe that you actually use language in the solving of problems I wish that you would write to me and tell me how you go about it."
• And in this week's Capital Gains, we look at what an analyst's job really is, and how investors use them.

Books

• Technological Revolutions and Financial Capital is a classic work on how new technologies go from prototype to ubiquity. It's a holistic view, starting with more concrete applications, broadening to how new businesses affect incumbents, and ultimately to how laws change in response to both the new technology and the downsides to the boom it creates. Fueling all of this is feedback effects with financial markets. Multiple feedback loops at different frequencies make these very hard to analyze, but the general outline of big technological transitions has been consistent over time.

Open Thread

• Drop in any links or comments of interest to Diff readers.
• What are some underrated alumni networks today? And which organizations are likely to produce them?

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Diff Jobs

Companies in the Diff network are actively looking for talent. A sampling of current open roles:

• A well funded seed stage startup founded by former SpaceX engineers is building software tools for hardware engineering. They're looking for their first marketing lead who will be responsible for marketing strategy, operations, and other content support. This person should be passionate about working closely with customers building satellites, rockets, and other complex machines. (Los Angeles)
• A startup building a new financial market within a multi-trillion dollar asset class is looking for generalists with banking and legal experience. (US, Remote)
• A VC backed company reimagining retirement wealth and building a 401k alternative is looking for fullstack engineers with prior experience in fintech. (NYC)
• A firm using machine learning to customize investments is looking for a data engineer. (NYC)
• A proprietary trading firm is seeking systematic-oriented traders with ML experience—ideally someone who has displayed excellence in DS and ML, like a Kaggle Master. (Montreal)

Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.

If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.

Where Does Stripe Go From Here?

You can break the life of a startup into few stages: it starts with early experimentation, leads to an initial product-market fit, and is followed by a period of peak variance in total value created—a time when a company's model works well enough for there to be a viable business, the rate of fire drills has reached a semi-manageable pace, and there's finally room to ask "What do we want this company to be when it grows up?" There is a big gap between initial success and defining a company's long-term role in the relevant ecosystem, and not everyone survives; Fitbit, for example, defined a category early on but eventually turned into a feature of more fully functional devices, while Apple has managed to continuously broaden its mission and redefine itself.

Stripe is at the stage where the core payment product works—10% of the world population transacted through Stripe in 2022—and businesses like Issuing and financial management are valuable enterprises on their own. And they have a reasonable path forward towards an even more valuable version of Stripe, just from a linear extrapolation of the current business. But markets are dynamic, and extrapolators eventually extrapolate themselves into a corner.

I recently chatted with Will Gaybrick, Stripe's President of Product & Business, to talk about what the company is doing now and how they're adding more degrees of freedom to their model. (Gaybrick's career is good evidence for the model of aiming for an unconventional Venn Diagram of skills; he worked at Blackstone before the crisis, spent a few years as a software engineer at startups, moved to Thrive as a partner, then joined Stripe as CFO. Taking an unconventional path to the CFO role enabled double-dipping on unconventionality by switching from CFO to product roles, and then to his current position. Of course, that's rendered slightly more linear by the fact that money is, depending on how you think of it, either the key raw material in Stripe's product or the product itself.)

One element of Stripe's initial model was that they specifically targeted startups. That is, and should be, a polarizing decision:

• Consider the downsides: these customers have, structurally, the highest imaginable churn rate, since they're designed to fail fast if they can't grow. Founders often have no idea what they're doing—or, put more nicely, there are a few dozen things a small company needs to be competent at, and small, high-variance teams will probably be exceptional at a few of them and inadequate at many more. Since startups start from zero, they have no vendor lock-in, and they tend to assume that most of the tools they use will be up to the standards of tools they care about (i.e. their typical expectation for software they use is determined by Zoom rather than Citrix, or by Expensify instead of Concur).
• And for theupside: those high standards mean that the quality of customer feedback will be extremely high. The high failure rate is offset by high growth, which is especially valuable for a company whose revenue is a function of customer revenue. And being a one-decision product for achieving competency in something critical—collecting money from customers who wish to spend it on your business's goods and/or services is, typically, a mission-critical task for a company—is a nice source of pricing power.

Let’s start with the churn issue. In a payments business, churn can be broken down in a useful way: there's churn from customers who go out of business, and churn from customers who suddenly require a different payments solution. (This is just a vertical-specific way to think about involuntary and voluntary churn.) That means a payments company that targets high-growth customers has selected a daunting task, but one that they can define with reasonable precision; consider the customers who are adding the most payments volume to Stripe's total each year. Estimate what that payment volume will look like over the next year, make a list of everything they will need at next year's payment volume. And ship it in the next twelve months.

The beauty of this arrangement is that it can simultaneously focus on a) the needs of existing customers and b) continuously expanding the addressable market. For example, imagine working with DoorDash, a company that handled $57bn in gross order value in the last four quarters, and that pays out a few billion of that to a few million individual drivers. Handling their payments is not a task to which a tiny payments company is well-suited. But if a tiny payments company starts working with an equally-tiny business like PaloAltoDelivery.com, then the tiny payments company’s task is to keep up with them—which is certainly not easy, but it’s a lot more achievable through incremental progress. And, as a result, it means that the maximum customer size, and therefore the immediately addressable market, grows as existing customers grow.

But one thing that happens as organizations grow is that they become, well, more of an organization. At a small enough scale, a business can work even if the org chart, employee handbook, and business plan all exist solely in one person's head. But eventually, companies develop some kind of shape that’s independent of the personalities of the founding employees. And that changes which enterprise products the company buys and how they buy them. This Steven Sinofsky essay on enterprise sales talks about part of the process:

Every enterprise sales person I have worked with begins to build out the physical and logical org chart from the first engagements. You want to learn the management reporting structure as well as the power. You want to understand the budget and decision making processes.

At first, companies use products that solve a discrete problem. As they grow, the approach evolves: instead of a binary solution, there are typically multple metrics a sophisticated customer optimizes for. Big companies do not pay the sticker price, and when they compare different payment solutions they're thinking in terms of the tradeoff between transaction volume, the fees they pay, the implementation cost on their end, implementation speed on the part of the partner, the risk of lock-in, and a host of other considerations. A pre-revenue company adopting Stripe is about as convenient as buying a can of Coke, but a large company choosing a payment provider is engaging in a transaction whose complexity is closer to the complexity of doing a leveraged buyout of the Coca-Cola Company.

And that has shaped Stripe's strategy. As the size of target customers grows, pure payments becomes more commoditized, but payments-adjacent software turns out to be more valuable.^[1] The business started as a layer on someone else's abstractions, by making it easier for sellers to get merchant accounts. But over time, it's been a successful effort to make the abstractions around moving money more tractable. The asymptote is that the global financial system will someday have a comprehensive, well-updated user manual, and it will live at stripe.com/docs.

How does Stripe get there? At one level, this is a fundamentally incrementalist project: there are many economic actors that want to spend money, many that want to receive it, and many whose processes are more tractable if those flows can be abstracted and automated. Stripe has retained a surprisingly rapid launch cadence; there's an unfortunate pattern with companies as they grow that features shipped in a given time period divided by headcount drops over time, and sometimes incremental headcount has negative productivity. There's natural entropy at work there (legal reviews make sense when you're operating in many jurisdictions and are big enough to be worth suing; channel conflict doesn't matter when you have hardly any customers; more interoperating systems mean more exposure to edge cases). But it can be resisted. Stripe tracks metrics around implementation, from coarse ones like pull requests per engineer to deeper ones.

At small scale, there's a strict tradeoff between responsiveness and planning: answer customer emails instantaneously whenever they arrive, and prioritizing the features customers ask for, means deprioritizing the features they don't ask for (or the features that would excite new categories of customers), and at an individual level hyper-responsiveness means never having long blocks of time for deep work. But at a larger scale, it's possible to provide that kind of coverage and still make investments in longer-term projects. The responsiveness makes sense; one of Gaybrick's observations about the business is that there are individual customer relationships that are responsible for a unicorn's worth of valuation. But it's not the only thing.

One case of that is security; the ratio of gross payment value to market value of the payments company is, effectively, a measure of how much that company needs to spend on security compared to peers who don't handle money—Stripe is a high-value target, and as its products increasingly enable marketplace-style businesses, its customers are high-value, too. That's a case where customers may like the investment in theory but be annoyed in practice if visible features get a lower priority than engineering investments that lower the risk of a serious hack. Either way, that tradeoff is worth taking in two long-term senses: it's obviously an existential risk to a payments company if the money entrusted to it is at risk, but it also helps the enterprise evolution if the company over-indexed to security relative to its peers. The customers who ask detailed questions about this can be of high value, indeed, and if one company has materially better answers than the others, then that company ends up with more pricing power.

One of the long-run questions about Stripe then becomes: how much room for improvement is left? Start with the assumption that Stripe's customers want to sell something and that their customers want to buy, and a lot of the value add in payments is just reducing friction to get things closer to an idealized state. But as it turns out there is a surprising amount of friction, even today: when Stripe looked at customers who had adopted payment elements and compared them to customers who hadn't, they saw a 10% lift in growth rates—that's a lot of value creation from tweaking a form! Link, which pre-fills customer payment information, takes 1/6th the time as other payment forms, and increases conversions. It would be unusual for the greatest gains in efficiency to occur at the end of a friction-reducing process, rather than close to the beginning, which implies that even for the core product, there's still room for growth.

In the long run, one of the challenges Stripe faces is defining the process it's built to improve. Abstractions are convenient when they hide unnecessary complexity, but dangerous when they hide something important. In his essay In the Beginning... Was the Command Line, Neal Stephenson uses the term "metaphor shear" to describe cases when a user interface hides something important. (At the time that he was writing, it was entirely possible to select all of the text of a multi-page document, delete it, press the "Save" button, and find that you had successfully saved your deletion and lost hours of work. That particular instance of metaphor shear is, fortunately, mostly gone, but others remain.) At some point, the task is less about making it easy for modern companies to interact with a variety of mutually-incompatible legacy systems, and more about using an understanding of how those systems work to build the infrastructure that succeeds them.

Elsewhere

Deal Flow

Nat Friedman (former CEO of GitHub) and Daniel Gross (AI at Apple, Pioneer, etc.) have bought roughly $100m worth of GPUs and are offering startups access to them on preferential terms. For context, cloud GPU providers CoreWeave and Lambda Labs would both charge roughly $50m/year for access to that much compute. Even when capital is scarce, investors try to offer something other than cash to convince startups to choose them, and one way to frame these offers is that it's partly an in-kind investment and partly a way to accelerate businesses at a time when speed is at a premium.

It's basically Charlie and the Chocolate Factory for AI startups: compete for a small chance to get access to something that everyone wants and that no one has a practical way to get—and, if you win, there's a chance you'll end up owning a thriving business.

Proprietary Data

One vision of large language model economics is that basic techniques will be open-sourced and commoditized, and the only enduring advantages will be 1) whoever can get the best economies of scale, and 2) whoever has proprietary training data. On the latter point, one of the questions that comes up is whether or not companies can really keep their data proprietary: The Information reported in March that Google's Bard was partly trained with ChatGPT data, and is now reporting that OpenAI used YouTube to train some of its models ($). AI has increased the value of anything that turns out to map between text and other kinds of data, which is one reason TikTok added a longer description field to videos last year. A few months ago, the smart bet was that the companies that would enforce their IP rights against AI models would be IP owners worried that their business would be entirely disrupted by AI. But it might actually turn out to be diversified big tech companies who want to use their own data to disrupt themselves first.

Beyoncéflation

Sweden's May inflation rate was 0.3% higher than expected, and their central bank attributes 0.2% of this to Beyoncé's world tour, which caused a spike in hotel prices. The typical line about small, open economies is that they're vulnerable to global changes in the flow of capital, but they can also be disproportionately affected by global changes in the flow of services. American pop culture hegemony is extremely strong, and American stars tend to be well aware of their own pricing power; when that collides with a comparatively small economy in a concentrated way, it can be a big enough deal to add some noise to economic data.

Pricing and Local Maxima

One of the most important things a company can test is how users react to sharing prices in different ways. When a large e-commerce site offers free shipping, and still displays a shipping cost on the checkout page offset by a free-shipping credit, it's because that leads to slightly more completed transactions than if shipping is omitted entirely.

But this kind of test-driven presentation can sometimes lead companies into a trap, where they get in the habit of hiding some costs until the last minute, and users start to discover that the price they think they're paying and the price they ultimately pay bear little meaningful relationship. Ticketmaster and SeatGeek are joining Airbnb in switching to 'all-in' pricing ($, WSJ): ticket sites have increasingly defaulted to having customers shop based on ticket prices that ignore fees, and then hitting them with fees when they try to make a purchase. Every incremental fee that's added in this way presumably hurts conversions but helps revenue more, on the margin. But every unpleasant surprise at checkout also means that some customers decide that the whole product category is a ripoff. Quarter to quarter, obscuring the high fees works; in the long run, the only question is whether companies that do this lose their customers directly, or whether irate customers eventually push legislators to ban the practice entirely.

Funds and Spoils

The FT looks at the divergent fates of two London hedge funds, Lansdowne Partners and Marshall Wace ($). They were founded around the same time, and in the mid-2010s their assets under management were in the same ballpark. But since then, Marshall Wace has successfully executed the pod structure while Lansdowne has been reluctant to give up enough equity in the management company to attract outside teams. This is a good illustration of the institutionalization of asset management. One side of this is that it switches from being a craft practiced by a small number of opinionated financial artisans into an alpha factory that's willing to pursue any strategy that promises another stream of uncorrelated returns. But it illustrates another aspect of that institutionalization: a company looking for talent needs to pay people with both the amount and the structure they're open to. Some funds have been profitable enough, and good enough at underwriting bets on individual managers' skills, that they can offer performance-based compensation and use fixed bonuses to get managers to make the jump. In other cases, though, a fund that can't be sufficiently confident in a manager's returns and that's reluctant to share equity in the overall fund-management enterprise will find itself perennially outbid. The artisanal, single-strategy approach still works; Lansdowne has $100m in assets per employee, and Marshall Wace is only 15% better on that metric. But Lansdowne ends up making a narrower and more volatile bet; a portfolio manager at either company would readily admit that the diversified business deserves a premium.

Unavoidable Drawdowns

Nassim Taleb has written some wonderful books on risk, with a focus on the idea that we underestimate the most extreme risks—at an individual level, that means people and organizations speculating in cases where they can face ruinous losses, and at a societal level it means brittle supply chains and total reliance on technologies few of us can understand.^[1] Taleb’s solution to our problem is an "antifragile" approach: position yourself to benefit from volatility, rather than betting against it, because volatility is inevitable.

But this raises an important question: when you try to address those risks, do you deliberately refuse to think about the absolute worst-case scenarios? It's important to do so. The possibility of total loss has a serious impact on the expected returns of any strategy, dragging it relentlessly closer to -100% with the passage of time.

Suppose you start with a pretty standard portfolio—you're using index funds to get exposure to a mix of bonds and equity; mostly equity when you're young and mostly bonds when you’re older. You set aside some money for fun stuff (maybe you buy shares in a company whose products you love, or short a stock whose CEO you loathe). And you allocate a little money to your emergency portfolio.

What does that emergency portfolio look like? It depends on the emergency you’re worried about:

• There are inflation-protected assets like farmland, where returns are tied to commodity prices.
• Worried about a specific bad outcome? Maybe you’ll buy puts on a Taiwanese ETF, or puts that pay off if the banking system is nationalized, or just own shares of a defense contractor or two.
• You might also hedge against personal risk: if people with your job title used to make a lot less money, your industry might have gotten more productive but it might also be going through a temporary boom. Shorting shares of companies that pay people like you the kind of money you’re making can be a nice hedge. (And it is a hedge: if a nervous Googler had shorted a basket of tech stocks ten years ago, they’d be hurting on that short position, but would be doing just fine in the aggregate if they’d been collecting stock-based comp from Google the whole time.)
• Gold is the classic “I don’t know what’s happening next, but I don’t think it will be good news” investment. Crypto offers a modern equivalent.

But if you think through each scenario, it's actually a way to address a worse-case but not the worst-case scenario. Physical gold, for example, retains some of its value even if the financial system collapses. On the other hand, the old coins we have are often recovered from hoards that someone buried and never dug up, often because of war, famine, plague, or revolution.

Someone who hedges their career risk by shorting their competitors still runs the idiosyncratic risk that one of those competitors will triumph specifically by defeating their employer—imagine a Compaq employee, worried that the PC boom will fizzle, shorting Intel and that one overhyped software IPO from Redmond.

Buying inflation-protected assets still makes some assumptions about which forms of capital will have value, and the security of claims against them. Members of the early 20th century English upper class tended to have portfolios weighted towards land and government bonds, exactly what you'd buy if you were certain of disaster but uncertain about whether it would be inflationary or deflationary. But after the First and Second World Wars, they got the worst of both worlds: inflation high enough to devalue the purchasing power of their bonds, taxes high enough to render the real return from their land negative.

There is, of course, a place for thinking about risks that are extreme in the sense that they lead to a financial wipeout for you, but not so extreme that they lead to the end of the social order that underpins your assets. For one thing, volatility drag is real: every 50% drawdown requires a 100% gain to get back to even, so volatility has a cost even when average returns are high.

This has actually played out not just at the individual or firm level but at the country level: some nations that industrialized rapidly chose to accept extreme economic volatility as the fair trade for rapid growth. And in many cases this worked out; South Korea grew rich and prosperous on the back of some seriously scary recessions in the 80s and 90s, before downshifting to a more stable model. But other countries with investment-intensive, borrowing-fueled growth hit macro turbulence that was more than they could handle; Brazil's performance looked similar to that of South Korea for a while, but some of the country's recessions have been so deep that recovery to the previous peak took over a decade and recovery to the previous trend never happened.

But outside the bounds of mere macro volatility, sometimes The Great Leveler shows up and portfolio beta suddenly matters less than survival. There are portfolios that are built around this—a nice luxury apocalypse bunker is, at a certain point, just prudent asset allocation. This is really just a death metal cover of Piketty's thesis in Capital in the Twenty-First Century: if the returns on wealth exceed the growth of the economy, then eventually everything is owned by whoever has been able to accumulate a critical mass of assets, unless something happens to either a) radically redistribute the wealth, or b) to destroy most of it, increasing labor's share relative to capital.

But that destruction turns out to be quite temporary! China's communist revolution was one of the most thorough economic levelings in human history, one so effective that the children of the elite were actually poorer than average for a while, but then recovered and by the early days of China's opening-up, and were better-off than ever. Similarly, in the post-Civil War South, the descendants of slave-owning families ended up about as well-off as other rich people, even though their land was seized, their currency depreciated to nothing, and the source of their wealth was banned by the Constitution.

There are some intangibles that persist even after cataclysmic events, and that's ultimately the only form of tail risk diversification that matters in a truly bad scenario. We're always hedging within the universe of possibilities we consider realistic. But some of the options we have are a function of intangible assets—skills and personal networks—which can't be expropriated. (They can, in a worst-case scenario, be destroyed, but they can't really be seized and used by someone else.)

Giving some thought to risk management, at the personal and societal level, is important. But a key lesson of Taleb is that risk is inescapable, and that extends even to the worst possible risks. Making investment decisions, whether they're purely financial or focused more on how you spend time, means placing a bet on an uncertain future. Ultimately, your bets have to move in the same direction history does, whether you’d like to or not.

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Elsewhere

Dumb Money

The term "dumb money" is a common pejorative that usually implies that there are many traders making the same bet based on either bad data or a lack of good data. But a broader way to think about it is that it's any class of valuation-insensitive investors—technically, the biggest category of "dumb money" is sophisticated institutional investors with too much leverage who have to drastically shrink or suddenly liquidate an investment. They're price-insensitive, and in a hurry.

One thing markets do is transmit reasonably-informed ideas into worse ones because those ideas have to get expressed imperfectly. For example, many investors have opinions on commodities, but aren't familiar with or don't have access to futures. So they use exchange-traded funds which systematically trade futures. This can lead to situations like what's going in in natural gas markets, where two ETFs, BOIL and UNG, own about 30% of the current month's contracts for natural gas. Betting on a commodity, especially one with wild seasonal fluctuations like natural gas, is partly betting on the direction of prices and partly betting on timing. ETFs simplify this, but that also means that they push more demand to whatever contract they're designed to buy. Traders can and do trade against this kind of demand, but the same seasonality means that each month's contract is a somewhat different fundamental bet, so the ETFs' trades can distort the market.

Adversarial Environments

The Diff has previously written semi-fondly about the crypto industry's attitude towards regulation. In one sense: for a product whose guiding ideology was somewhere between libertarianism and anarchism, and whose first killer app was mail-order narcotics, it's a surprisingly buttoned-up industry with an refreshingly friendly attitude towards regulations. On the other hand, one reason for this is the high attrition: the sloppy exchanges get hacked by criminals, and the well-run exchanges are sometimes run by financial criminals. And sometimes there's convergence: BTC-e was a crypto exchange focused on Russian users, which was seized in 2017. As it turns out, one of the exchange's operators was one of the hackers who breached Mt. Gox, another crypto exchange, leading to the latter's collapse. The magic of ongoing improvements in blockchain analysis means that the industry is always getting retrospectively dirtier even as it cleans up its act in the present.

Coasian Bargains in Immigration

Global trade depends on the fact that different countries have different comparative advantages, related to natural resources, physical capital, institutions, and historically contingent factors. This usually shows up with the trade of goods and services in the private sector, but sometimes public sector actions can highlight it. For example, the EU is offering Tunisia €1.2bn in subsidies and loans in order to reduce immigration from there to the EU ($, FT). One way to look at this is that Tunisia has a comparative advantage in supplying a basic standard of living to people within its borders; Tunisia's cost of living is about 40% that of France, so even with some frictional costs, €1 of public services can go further there. It's a Coasian bargain in another sense: the EU countries might prefer to have less immigration, or to have it happen in a more controlled way, but if the country that can make that happen is either less interested in accomplishing that goal, or ill-equipped to, paying them can be the most cost-effective approach.

Asset Allocation

CalPERS, the $442bn public pension, is considering adding up to $5bn to its venture allocation ($, FT). There are many moving parts to this: CalPERS' argument is that within the broader private equity category, venture tends to have better returns. But venture is also levered to tech in general, which has outperformed in the last decade. One of the difficulties in asset allocation is distinguishing between an increase in the average expected return for a category and an increase in the entry price for that asset.

But what's also going on is that venture funds are in an annoying prisoner's dilemma. Each individual fund wants to keep its assets marked based on where valuations were two years ago, when they were deploying substantially more capital. But one result of that is that since public equities and bonds have declined, investors are more overweight venture than they were before, and getting back to even means writing smaller checks to venture funds, or skipping them entirely. And that puts more pressure on private company valuations, further growing the gap between the price at which they're held on the books and a realistic market price. So even if CalPERS' decision is not the right one on its own, it could be a self-fulfilling prophecy that pushes private company valuations up a bit and makes the market function better.

Lock-In

Apple is adding more OS-level features that create iPhone-specific interactions in apps. Part of Apple's strategy on the hardware side is to use tools like iMessage to get software-based lock-in ($, The Diff)—depending on how you attribute the incremental profits from new iPhone and Mac sales, iMessage is arguably worth $10s of billions, making it a fairly successful social network on its own. And once those economics work, the natural path for Apple is to extend them more.

Diff Jobs

Companies in the Diff network are actively looking for talent. A sampling of current open roles:

• A VC backed company reimagining retirement wealth and building a 401k alternative is looking for fullstack engineers with prior experience in fintech. (NYC)
• A new health startup that gives customers affordable access to preventative care and lifestyle interventions seeks a founding engineer. 7+ years of JavaScript experience preferred (TypeScript is ideal), and payments experience is a plus. A great opportunity for anyone excited to make healthcare better by treating problems cost-effectively before they're catastrophic. (US, remote; Austin preferred)
• A startup building a new financial market within a multi-trillion dollar asset class is looking for a senior ML engineer, especially someone interested in using LLMs to make unstructured data more tractable. (US, remote)
• A company using Web3 to decentralize customer loyalty programs is looking for a founding senior engineer with Solidity experience and an interest in brands and the arts. (Brooklyn)
• A hedge fund is looking for an experienced alternative data analyst who can help incorporate novel datasets into systematic strategies. (NYC)

Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.

If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.

• C Trombley One reviews Hyman Minksy's John Maynard Keynes. The big focus of the book (and the review) is the role of uncertainty in understanding the economy—both in terms of how economic actors react when circumstances change and how they act given the fundamental uncertainty of the economy. While perfect information always makes models simpler, there are always more ways to go meta: a core part of Minsky's model is that individually rational behavior can lead to collectively irrational results. Financial markets are a powerful channel for this, because they're all about temporarily reifying abstractions, and in complicated ways. For example, a dollar is worth a dollar as long as everyone believes this is true, but a share of stock is perfectly convertible to cash at the market price, but not if everyone treats this as a given all the time. Minsky (and Trombley) both demonstrate something fun: People on the left can appreciate capitalism in a way practicing capitalists can’t, because to them it can be an aesthetic experience rather than a practical one. It’s the same sense in which it’s easier to enjoy rhetoric from Pericles than from a charismatic figure alive right now—today you’re too distracted by the practical outcome of the speech, but it’s not like you’ll lose any friends today by supporting the wrong side in the Peloponnesian War.
• Marc Andreessen on the case for being optimistic about AI: much to agree with, especially on reasons to be skeptical of some aspects of AI safety being more risky than what they're aiming to fix. And there are some parts of the piece to be skeptical of. There is a real case for AI destroying jobs, eventually (in the short term, employment can go up because the value of doing any kind of information-processing task is now based on the net present value of future AIs being able to do it themselves). But one important point the article makes on the economic impact side is that the sectors with the most technology-driven change also tend to create the most equality in terms of access to products—like the information that's freely accessible via Google—while the industries that make inequality more salient are slow-moving ones where nothing changes except the price.
• Steven Sinofsky has a masterpiece on platform businesses, worth reading for the tiny minority of people who will create a platform and for the much larger number who build something dependent on platforms. Among other things, it has a nice taxonomy of different kinds of platform businesses, a good description of the traps proto-platforms fall into, and a sketch of the lifecycle platforms go through. One nice note here is that the piece does not insist on being definitive; at one point, it mentions an ongoing argument with Bill Gates over how to best operate Microsoft as a platform business. The details of these models are still up for debate because the sample size is so small and because the examples are necessarily heterogeneous.
• Josef Adalian and Lane Brown write about the decline of the streaming business, as seen be screenwriters, directors, and operators, in Vulture. It's amazing how quickly people in any industry can get accustomed to temporary circumstances, whether it's enjoying nearly-free Uber commutes in the mid-2010s, easy credit in the mid-2000s, lucrative stock-based comp until 2022, etc. In Hollywood's case, one driver was that streamers were all fighting for share, and there was a finite supply of TV and movie production talent that got bid up. The downside was that streaming economics are more opaque than traditional TV and movies; shows get valued based on how they affect signups and retention, and that's much less direct than their ad revenue or box office performance. (It also makes residuals even harder to calculate: even Netflix probably doesn't have high confidence in how your viewing choices today affect your propensity to unsubscribe two years from now.)
• Zachary Carter profiles economist Isabella Weber and her efforts to rehabilitate price controls in The New Yorker. "Price controls," in this case, can refer to a variety of different policies: one that's highlighted in the piece is a plan that gives German households and businesses subsidized access to natural gas up to a quota, with any excess consumption priced at the market price. In this case, though, it's a one-sided price control: the government, rather than the companies, eats the gap between market price and the controlled price. What that amounts to is using the government rather than households as a shock absorber, which is at least somewhat defensible for a good with inelastic consumption patterns. (But the policy only really works if it ultimately incentivizes people to switch to other energy sources, or if the shock is truly a one-off—and politically, taxpayers might find it annoying if a large line item in the government's budget was for subsidies to big energy companies, even if those subsidies offset government-imposed costs.) Weber uses the US's price controls in the Second World War as an example of when price controls work, but that's very much a special case: the point of a war economy is to increase production of military-related goods and then control inflation by crushing demand for goods that civilians need. It can work for a while, especially in a country that's feeling a surge of high trust because of a war, but it's a least-bad approach to dealing with bad news, not a good ongoing default.
• In this week's Capital Gains, we look at why economists dread deflation, and why voters are more irate about inflation. And note that not all economists dread deflation.

Books

• Osman's Dream: The History of the Ottoman Empire: The Ottoman Empire can fit into many different frameworks. In one sense, it was a classic example of a steppe nomad tribe rapidly expanding; in another sense, it was a case of the pattern within that where the steppe nomads militarily conquer a country so prestigious that the country culturally conquers them right back from within (after taking over the city of Constantinople and ending the Eastern Roman Empire, Mehmed II added ‘Caesar’ to his list of titles. It was also a multiethnic empire that controlled different territories with varying degrees of centralization; sharp lines on old maps are always something between an artistic fiction and an expression of the limits of contemporary artistic ability, since pre-modern governments had variable control of different parts of territory. The running theme in the book is that it’s astounding that the empire lasted as long as it did. Even during the period when the Ottomans were relentlessly conquering their neighbors, the country faced constant internal rebellions, religious disputes, pretenders to the throne, etc.

Open Thread

• Drop in any links or comments of interest to Diff readers.
• Inspired by Matt Mandel: have you worked at a high-performing company that insisted that new employees read a particular book? What company/industry, and what was the book? (Feel free to answer via email; if I get good answers I will compile them but will default to just noting the company and book unless you specifically ask to be credited.)

Reader Feedback

David Khoo notes an alternative explanation for the wine store collapse story from last week's Longreads:

The Sherry-Lehmann wine store affair sounds like classic long firm fraud: Take over an established business and its valuable commercial arrangements with suppliers and customers, then use those arrangements to defraud them. Order goods and don't pay for them. Take payment for goods and don't deliver them. Bank on the trust the previous owners had built up with them to delay, make excuses and continue the con until breaking point, then disappear with the money.

...

If anything, wine stores seem like a perfect target for long firms because there is such a long gap between paying for a product and receiving it. A business where customers routinely pay millions today for this year's vintage with delivery in 3-4 years is raw red meat for conmen.

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Diff Jobs

Companies in the Diff network are actively looking for talent. A sampling of current open roles:

• A company building ML-powered tools to accelerate developer productivity is looking for a mathematician. (Washington DC area)
• A well funded seed stage startup founded by former SpaceX engineers is building software tools for hardware engineering. They're looking for a UX/frontend engineer interested in designing and developing software collaboratively with satellite, rocket, and other complex machine engineers. (Los Angeles)
• A firm using machine learning to customize investments is looking for a data engineer. (NYC)
• A vertically integrated PE-backed cannabis company is looking for a production analytics manager to optimally allocate plant biomass. Excel wizards preferred. (Little Rock, AR—no remote, but relocation assistance is possible)
• A company building zero-knowledge proof-based tools to enable novel financial arrangements is looking for a senior engineer with a research bent. Ideal experience includes demonstrations of extraordinary coding and/or math ability. (NYC or San Diego preferred, remote also a possibility.)

Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.

If you’re at a company that's looking for talent, we should talk! Diff Jobs works with companies across fintech, hard tech, consumer software, enterprise software, and other areas—any company where finding unusually effective people is a top priority.