Newsletters and New Media Economics

Plus! Coming Attractions, Fortnite and Apple, Apple's Other App Problem, Theaters and Two-Sided Networks, AI Economics, Wealth Taxes, and much more...

This is the once-a-week free edition of The Diff, the newsletter  about inflections in finance and technology. The free edition goes out  to 10,551 subscribers, up 185 week-over-week. This week’s  subscribers-only posts:

In this issue:

Newsletters and New Media Economics

Paid newsletters are popular enough that the business is getting its  fair share of is-this-a-bubble-or-what? pushback. One view is that  newsletters are quickly becoming a saturated market. Sure, Andrew  Sullivan can quit New York Magazine and instantly have a six- or seven-figure subscription income (the Substack leaderboard rounds the numbers a bit, but that range makes sense). But if every writer at NYMag quits, and they all  charge $5/month, the cost of recreating the magazine is multiples of  what it costs to subscribe. To get a year of Andrew Sullivan, you pay  $50/year. To get a year of everyone-left-now-that-he’s-quit, you pay… $20/year for your first year, and $60 thereafter.

The math doesn’t add up—at least as long as consumption patterns are  constant. And clearly they’re not. Paid media is subject to three  pressures, which work in tension with each other to determine the  optimal structure of a media company—whether it’s, at one extreme, a  giant company with a flat cost for every conceivable piece of  information, or at the other, a series of one-person companies with  highly variable pricing. Those forces:

  1. Bundling
  2. Distribution
  3. The convexity of knowledge

Bundling Consolidates Media

There are dozens of good explanations of bundling. Here’s a very relevant one,  from the creator of the successful Everything Bundle on Substack. Bundling works  when customers have heterogeneous tastes and the cost of creating one  more copy of the product is low. A bundle essentially lets a group of  newsletter-writers dynamically price-discriminate: most readers are  subscribing because one or two components of the bundle are great and  the rest are nice-to-have, so Everything’s $20/month sticker price is  implicitly charging something like $15 for one newsletter in the bundle,  $1 for another, $0 for another—but which newsletter is the premium product within the bundle varies from subscriber to subscriber.

As Baschez puts it:

The thing that makes bundles work is they eliminate  waste. What waste? The wasted demand of all the people who want access  to each newsletter a little bit, but not enough to pay the market price.

Most high-circulation publications are bundles. Some people read The Wall Street Journal  for news about markets, others for news about deals, others because  they like the editorials. Bloomberg is an incredibly broad bundle, and  part of its utility is that any investor who spends all their time on  one kind of asset occasionally needs to take a peek at another; an  equity investor sometimes needs to know what’s going on with bonds,  currencies, and commodities, but doesn’t want to spend much on a  dedicated product just to track them. An algorithmic trader might get  99% of their Bloomberg utility from data feeds, but 1% could glance at  its news aggregation service 1% of the time to understand what headline  made the algorithm break that day.[1]

And that points to another benefit of bundles: they lower customer  acquisition cost, especially when selling a product to additional users at the same company,  because it’s much easier to convince a company to go from 1  user to 10 than to go from 0 to 1. When customer acquisition costs are  high, the bundle naturally expands to meet every need the biggest  customers have.

But this raises a natural question: why is the Everything Bundle not literally everything?  Why doesn’t it have a higher price point, and include all the latest  developments in sports, celebrity gossip, astrophysics, and limericks?  Because bundles benefit from clusters of adjacent readers. The  readers within the cluster can have heterogeneous preferences, but they  need preferences for the same approximate kinds of things, or it’s too  expensive and time-consuming to attract them. There are plenty of  publications that bundle content, but it’s adjacent content: beauty,  fashion, and celebrity news; programming, venture deals, and career  advice; politics, business news, and very well-written obituaries; etc.

One way to look at it is that a bundle has to appeal to the lowest  common denominator for some very specific definition of “common.” When  local newspapers were monopolies, their bundle was everything the  average person in a given area would care about: local happenings, local  sports, the weather, obituaries again, and a sprinkling of national and  international updates. But the exact contents of that bundle only  worked for a specific locale; telling people about the weather “on the  east coast” was not exactly valuable, while today’s weather in Boston  was important to Boston Globe readers.

Bundles tend to grow until they reach a highly profitable mature  state—at which point any change in the underlying audience, or the  availability of competing products, seriously weakens their economics.  The bigger a bundle gets, the more likely it is that a subset of users  are all paying for basically one piece of the bundle, which could be  sold separately at a better price. And as soon as a bundle is partially  unbundled, there are two options: stop offering the part of the bundle  that now has a competing single-purpose product, at which point the  bundle switches from optimally-priced to overpriced, or keep offering it  and accept lower margins. Bundles grow gracefully and shrink painfully.


Another driver of aggregation is distribution: what’s the optimal way  to get a given piece of content to the audience most likely to pay for  it? Media companies have complained that Facebook and Twitter took over  their distribution: instead of selling an entire edition, media  companies' outputs get deconstructed into their individual components  and distributed to the audience that wants exactly that.

For commodity news—a summary of what Biden or Trump just said, or  what happened at a game, or what Apple put in a press release, that’s  especially brutal. There aren’t many people with a competitive advantage  in interpreting every piece of news in a given category. (Nobody hears a  quote from Trump and says “I’m going to wait for an article by  so-and-so before I decide whether or not I agree with this.”) But for  stories that require some editorial judgment—when the problem is  choosing what to write, not being the first to publish—social media’s  effects on distribution are a bit different.

In that case, when distribution is through social media and peer  recommendations, it belongs to the writer, not the publication. When you  read the Wall Street Journal because someone throws it on your lawn every morning, a Journal  writer can’t go independent. But when you see the same articles because  they make it into your Facebook news feed, you may end up following the writer  directly—they start to take control of their distribution. Media  companies have encouraged writers to promote and discuss their work on  Twitter, which has a short-term positive payoff in traffic. It’s a bit  like outsourcing: companies used to outsource the most annoying,  labor-intensive part of their supply chains, but the companies getting outsourced to moved up  the value chain until they could build a competing product and sell it.  Xiaomi and Huawei’s snazzy smartphones exist in part because Apple and  Samsung didn’t want to directly manage hundreds of thousands of people  working on assembly lines. (Edit: a reader correctly points out that this example would be more compelling if it were actually true. A more accurate example: Samsung assembled smartphones for Sony and Toshiba before building its own brand; Xiaomi and Huawei benefited from the growth of the phone assembly business in Shenzhen, but were not themselves contract manufacturers.)

Controlling distribution for a product with high fixed costs and low  marginal costs is powerful, because it means not just getting content to  anyone who’s interested, but restricting it to people who  express their interest in dollars. For a large media company aiming at a  broad audience, deciding on price is an exacting science, and there are  all sorts of price-discrimination tricks for getting users at the right  time. (For example, everyone from Amazon Prime to The Economist to  Netflix tries to get college students in at low prices—in Netflix’s  case, a price of $0 through a shared login—in order to raise the price on them later.) Fiddling with the pricing and offer dials to maximize  conversions pays off at a large scale, but simple approaches work fine  at a small scale, because individual writers can have a monopoly on  their output, and monopolists can afford mistakes.

The Convexity of Knowledge

The writer-as-monopolist is an important economic fact. One thing the  most popular tech/finance newsletter writers have in common (and here  I’m thinking of Ben Thompson and Matt Levine)  is a set of obsessions they keep coming back to, mental models they  keep applying, and themes they keep revisiting. If Facebook tries to  make inroads on Google’s revenue, you know Ben will have an  Aggregation Theory lens for it; if a company gets sued for doing  something clever with credit default swaps, Matt Levine is guaranteed to  walk through the math and incentives at play. It’s very hard to  outsource this kind of thing! Knowledge has compounding returns, but  mostly when it’s contained in the same skull; two people with half the  expertise yield well under half the insight.[2]

This is well-understood in programming: there’s a whole book  on the fact that when you add programmers to a team, the time spent  explaining the project to them can more than offset the extra time they  spend working on it. Distributed computing is hard enough with actual  computers; it’s far harder with human beings.

This implies that in niches where there’s too much information for  any one person to absorb, the most economically efficient outcome is for  media coverage of that niche to be dominated by exactly one person, who  works fairly hard and has more comprehensive knowledge of the topic  than anyone else. If distribution costs were high, the result would be a  specialty publisher that hires all these experts and then flogs the  results of their work, but as distribution costs decline, it makes more  sense for them to go independent.

Patrick McKenzie has suggested this in one domain:

But it can apply outside of finance. It will probably start  in finance—that’s where the money is—but there’s no reason it can’t move  to other topics, too. Everyone who needs to have an opinion on CFIUS,  or needs to know every major development in mRNA vaccine research, or  needs to know how GPT-3 is being used in production, can afford to pay a  lot to be sure they’re not missing anything.

And this is a good counterpoint to the newsletter-skeptic argument  about newsletter fatigue. Yes, many people spend entirely too much time  reading newsletters—but that means there’s a market for compressing  their two hours of somewhat indiscriminate, redundant reading into ten  minutes of tightly-focused reading instead. For sub-enterprise price  points (i.e. under about $500 a head), the cost of a newsletter in time is bigger than its cost in dollars.  To put a little more math on it: paid readers open my newsletter about  75% of the time. At 10 minutes per newsletter, 250 issues a year, and a  75% open rate, that’s about 31 hours of reading per year. At the current  US minimum wage of $7.25, the opportunity cost in time is almost  exactly in line with the price of a subscription to The Diff.

This is all very good news, for writers and for readers. Filtering  news and adding useful commentary is a nontrivial task, it’s hard to  scale, and scaling it in one domain doesn’t imply skill in doing it  somewhere else (if I switched places with someone who wrote a sports  newsletter, we’d both lose all our readers). But the subscription  newsletter model encourages people to identify niches where the  signal/noise ratio is out of whack, and charge a premium price for a  higher-signal publication.

Bundling reacts to differentiated desires by creating a less  differentiated publication that’s fairly valuable to everyone. But as  the cost of the reader’s time rises, focus pays off. And the  subscription newsletter model makes it easier than it’s ever been to  profitably focus on exactly one topic, and build a one-person monopoly.

[1] This piece focuses on one piece of the media business: paying  money to get content. There’s another motivation, though: paying as a  form of patronage. At low price points, patronage dominates:  “This is nice, and I’d pay the price of a latte every month to support  it,” is a motivating factor for some purchases. At price points beyond  that, patronage only tends to work if it’s paid in exchange for either  a) recognition, or b) additional hard-to-scale services. Call it the Tote Bag Line: past about $60, donations need to be paid back with something that advertises the donor’s generosity.

[2] I suspect that this is one reason the correlation between income  per hour and total hours worked has been rising. The best specialist in  any given domain gets a higher incremental return on one more hour of  experience or learning than the next-best, because they can fit new  information into more models, find more analogies, and make more  connections that encourage further research.

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Coming Attractions

Next week’s newsletter will be a series, inspired by a question from Andrew Walker of Yet Another Value Blog:  historically, some of the top companies by market cap saw their value  seriously impaired—to zero in some cases, down 80% or more in others,  “dead money” for a decade in others. It would be very odd if 2020 were  the first time investors in the very biggest companies didn’t see at  least one of them underperform. So, one day a week next week we’ll be  writing a hypothetical obituary for a large-cap high-growth name,  looking back from 2030 at what went wrong.

And at the end of next week, I’ll be trying something new: a Zoom  chat with paying subscribers for open-ended Q&A. Bring questions!


Fortnite, Apple, and Platform Wars

Epic, publisher of the wildly popular Fortnite (with 350m registered  users as of May) gave players a novel offer for in-app purchases: they  could buy in-game currency for $9.99 through Apple, or buy the same  amount directly from Epic for $7.99. This is strictly against Apple’s  rules: Apple requires developers to route payments for most digital  goods through the App Store, giving Apple a 30% cut. So, in keeping with  the company’s hard-line approach to off-platform transactions, Apple promptly removed Fortnite from the app store.

And then the fun started.

Within minutes, Fortnite had launched a response on two fronts, legal and PR.

What’s admirable about this is that usually Apple is the  company a few steps ahead of the competition. They like to buy up the  global supply of key components before they launch a new feature, so  their competitors take longer to catch up; they strike early deals for  new products that turn out to be lucrative for them later; they’ve  adapted to the maturity of the smartphone market by selling pricier  phones and adding more services revenue. And this time, Epic was the  better-prepared company.

It will be hard for Epic to press their advantage. While they call  Apple’s policies abusive, and say that they stem from a monopoly, Epic  gave users the same payment option on Android, and were promptly removed from the Google Play store, too.  It’s hard to say that a company’s behavior is uniquely bad when it’s  not unique, although I suppose they could argue that Apple and Google  are colluding.

And, paradoxically, their PR strategy won’t keep its momentum for  long because Apple’s response doesn’t especially inconvenience current users.  Apple removed the game from the App Store, but existing users can still  play it, so instead of millions of fans being annoyed that they can’t  access their game, Apple is inconveniencing future Fortnite fans, and  the small number of users who buy an additional device and want to use  it for Fortnite. So Epic has one day of momentum, but it will diminish.  Fortnite is valuable to Apple and Google, but Apple and Google are far more valuable to Epic, so ultimately their bet rests entirely on their legal case—whose arguments have been under consideration for some time, with no effects so far.

Apple’s Other App Problem

The looming TikTok ban has gotten a lot of attention in the US  because of the app’s popularity, but a WeChat ban could be more  significant. The prospective ban is vague, but if it prevents Apple from  offering WeChat on phones in China, it will threaten the company’s $44bn in China sales.  Apple has an interesting protectionist argument here: since iPhones  assembled and sold in China still use US-designed software and hardware, they’re  positive for the US trade surplus, so a wide-ranging WeChat ban would  run counter to the economic nationalist argument for a more limited  US-only ban.

Theaters Restart the Two-Sided Network

I’ve been writing for a while about the double-bind movie theaters  are in: studios will consider a theatrical launch rather than a  streaming launch if movie theaters start getting visitors again, but  visitors come to watch movies. AMC’s solution is to move very far along  the demand curve: offering 15 cent movie tickets for re-releases of prior hits.  This is actually a clever way for theaters to show that they can still  fill seats, and it’s a good way to simplify the consumer decision when Tenet finally comes out: instead of asking “Do I want to see this movie?” and “Do I feel safe coming back to theaters?” they can just ask the first question.

Singapore, Reconsidered

There’s a great piece in Palladium  on some of the complexities of the Singapore Story. Singapore’s  development has many lessons for other emerging markets, but they can’t  be applied blindly, and even Singapore’s hyper-competent civil service  did not have a perfect record:

History provides us with a natural experiment. In 1994,  Lee Kuan Yew and Chinese Vice Premier Li Lanqing signed the “Agreement  on the Joint Development of Suzhou Industrial Park.” Under the  agreement, Singapore would maintain a 65% ownership stake in the project  and develop the city of Suzhou into a modern industrial powerhouse—all  running on Singapore’s public-administration and industrial development  expertise.

Yet by 1999, Lee had failed in Suzhou. Five years into the project’s  20-year development plan, Suzhou Industrial Park had only attracted $754  million dollars of investment out of target of $20 billion, 5,000  residents out of target of 600,000 and 14,000 employees out of a target  of 360,000.

Yet competition for foreign direct investment from nearby Suzhou New  District—a smaller, older, and less-supported development that Singapore  previously dismissed—proved too fierce. Singapore’s elite group of  civil servants simply could not navigate China’s multi-level government  and apply the Singapore model at scale.

The Singapore Story, as it turns out, was very Singapore-specific.  The blueprint can’t be copied blindly, any more than Lee Kuan Yew could  have made a list of everything Renaissance Venice or medieval Hamburg  did, without adapting it to modern circumstances.

Wealth Taxes and Peak California

California is considering a tax on wealthy residents.  The tax, they promise, would only affect 30,000 people out of a  population of just under 40m. But it would have an important impact on  the state’s growth. California’s tech economy is driven by a network  effect, where people who made their fortune in one generation of  technology invest it in the next generation. Discouraging this process  would not have a terrible impact immediately, but it would  lower the odds that the next great tech company was founded in  California. Further, the tax exempts directly-held real estate, so it  would push California’s housing prices even higher—which acts as an  implicit subsidy for companies that can generate cash flow and pay high  taxes, relative to startups that burn capital and pay employees in  illiquid equity. This proposal accelerates every piece of bad news from  my Peak California piece last year.

A16Z Revisits AI Economics

Earlier this year, A16Z put out an important piece  explaining why AI companies have worse unit economics than other  startups. The cost of compute and training models remains high as a  company grows, so gross margins don’t hit the levels other software  companies have come to depend on. Now they have a follow-up, on how AI companies can achieve better economics.  The Straussian reading is that the companies that are best able to  profit from AI are the ones that a) have many lines of business that  allow them to share functionality across domains, and b) have an  established user/customer base, so they can pick which long-tail  problems to address based on where returns will be favorable. A16Z, in  other words, is treating Big Tech as the natural beneficiary to AI.

Bullet Trains and China Stimulus

China plans to double its high-speed rail network by 2035 ($). (Note that this article appears in Nikkei,  Japan’s most read business publication, but somehow avoids being bitter  even though China’s high speed rail economics are driven by amortizing  the cost of borrowing Japanese IP in this area.)

In past crises, China has stabilized its economy by borrowing to  build infrastructure when there’s a demand shortfall. When China lagged  other countries, this was a reasonable model, but on a road- and  rail-mile per dollar of GDP basis, China is already at rich-world  levels. So the only way to justify large capital projects is to leapfrog  ahead of other countries. Two side effect to watch: