A payments thought experiment

Imagine two variants of Uber:

Uber A

• hail via smartphone app
• pay by swiping a credit card from your wallet

Uber B

• hail by calling an operator
• pay “touchlessly” with a saved credit card

Which would you rather own? What does that tell you?

On the Integrity of Firms

Companies, like people, have an integrity all their own. To be clear, I don’t mean this in any moral sense, but in the latinate sense: the presence of consistency. A company with integrity fits together in the manner of a puzzle solved—everything in its right place, self-organized and systemically strong.

Notice that nothing in that definition admits morality. A company, like a person, can have morality, but it’s not a requirement for success. An amoral company with integrity is a more powerful engine than a moral company without. Nor does the presence of integrity imply the presence of any specific practice or process. There are as many ways for a company to have integrity as there are companies on Earth. When it comes to systemic consistency, there’s no right or preferred or best way—there’s just what has worked for others and what might work for you.

I say this all in the context of the Great Yahoo Centralization of 2013, in which Marissa Mayer decided that the integrity of her company was furthered when employees worked from the office, and the Internet decided that she was wrong. But it’s in the nature of complex problems to have no ready answers. What works for one person or company in one situation is unlikely to work for another person or company in another. Perhaps it’s true that this year’s studies tend toward remote work as the productivity question’s answer. Last year it was open offices, and before that it was private rooms. Before that, bullpens, and before that, ale houses probably.

Regardless, to say that you have the answer to Yahoo’s problems and Marissa’s is not it is egotistical in the extreme. Again, here I write in the latin sense: the drive to believe that the choices we’ve made are the right ones, not just for us, but for everyone. But there are no universal laws in startups, and no matter how many books Steve Blank sells, that is and will remain the case.

In business generally—and startups particularly—no decision is made in a vacuum; every choice resonates through every other. Culture informs hiring informs structure, and right back around again. There are any number of legitimate reasons for Yahoo to call its troops home. Whether that decision was “good” or “bad” won’t be decided by productivity studies or anecdotes about what works for one company or another. No, whether the decision was the right one depends on whether the action added to Yahoo’s consistency—whether it was made with an eye toward integrity. And that, for now at least, is a question none of us is in a position to answer.

The Mess We’ve Made

marksbirch:

When framing any debate on the value of college, it is worthwhile to see how the recipients of college degrees are faring. Apparently, not so well. As Clay Shirky states:

The value of that degree remains high in relative terms, but only because people with bachelor’s degrees have seen their incomes shrink less over the last few years than people who don’t have them. “Give us tens of thousands of dollars and years of your life so you can suffer less than your peers” isn’t much of a proposition. More like a ransom note, really.

The debate regarding the future of higher education is becoming more animated, but one thing is clear at this point. We cannot continue to support a system where the costs vastly outweigh the benefits. The system will be disrupted.

Any discussion of rising higher ed tuition prices is incomplete without an examination of the problem’s underlying causes. Working under the assumption that (1) the quantity of Americans seeking higher education hasn’t grown dramatically in the past decade, and (2) the quantity of universities providing higher education hasn’t shrunk dramatically in the past decade, a natural place to look for upside price pressure is an increase in the volume of funds available to each person seeking higher education. Assuming that recent high school graduates and their parents haven’t decided to reallocate a massive chunk of their personal assets to higher ed in the midst of an economic crisis, let’s take a look at the other side of the personal balance sheet.

Ah, right, debt. But now the question becomes: who’s willing to originate so much debt during a credit crisis? I doubt the banks believe that college students are the safest credit bet out there, and as it turns out, they don’t. Here’s a chart of student loans directly owned by the federal government.

Terrifying. And that doesn’t even include the privately held loans they’ve guaranteed.

How did this happen? The answer’s complicated. Student loans have always been a boon to politicians from both sides of the aisle, but prior to the crisis that boosterism was limited to incentivizing private lenders via default and interest rate subsidies. When the crisis hit, those incentives were overrun by larger economic forces, and the student loan market appeared poised to crater just as the mortgage lending market had. In response, Congress stepped in, passing the Ensuring Continued Access to Student Loans Act of 2008.

Under ECASLA, the government quickly purchased some $150B in outstanding loans and capitalized interest from private lenders, which explains for the inflection beginning in January 2009 and continuing through to mid 2010. The discontinuity in October 2009—and October 2010—results from the Purchase Commitment Program in the legislation, by which the government granted lenders a put option on their loans that expired at the end of September each year.

The second major cause is an obscure rider to Obamacare called the Student Aid and Fiscal Responsibility Act. This act ended the concept of federally subsidized loans in favor of direct loans from the federal government. At the same time, the government began promoting a number of novel repayment programs that linked repayment schedules to a borrower’s earnings, thus pushing payment obligations far into the future. In effect, the government had begun granting the educational equivalent of NINJA loans with unpredictably back-loaded payment structures while covering the interest accrual on these loans with taxpayer funds.

So what did all these private incentives, subsidies, transfers, purchases, direct loans, and interest coverage buy us? Exactly what they were designed to buy: a shitload of higher education.

(Note that the change in enrollment rate since 2000 lends support to our original assumption about changes to the quantity of students. 5% growth in participation multiplied by the change in college-aged students isn’t a small change, but it certainly doesn’t explain a nearly 200% change in price, especially if the supply of schools underwent similar growth.)

Of course, there were side effects, such as the aforementioned increases in tuition. Wherever cheap money appears, businesses built to hoover up those funds tend to follow. When the government and private lenders began making it rain, our institutions of higher education/money management companies with a small educational tax shelter knew the time had come to shut up and take the money.

Now, when non-profit or state-funded schools raise tuition just because they can, that’s not exactly good behavior, but it’s also not the heart of the problem. We can debate whether such institutions are properly preparing students for the workforce, but given the macro circumstances, they’re not doing an unusually awful job. 90% of them are repaying their loans on time and their unemployment rate closely tracks the national average. That’s not cause for celebration, but it also doesn’t scream ‘national crisis.’

For-profit universities, however, are an entirely different story. So different that it seriously damages the all-too-prominent “startups are the best way to disrupt higher ed” narrative. 96% of students enrolled at for-profit colleges take out loans (compared with 57% at private non-profit schools, 48% at public schools, and 13% at community colleges). Unsurprisingly, 30% of for-profit schools draw more than 80% of their revenues from federal loans. It’s no wonder that a number of very smart hedge fund investors (especially value-oriented ones) are long Sallie Mae.

And what has all that money bought us? Not much it seems. For-profit schools employ ten times as many recruiters as career services counselors, spend 1/4 - 1/8 as much per student on instruction as non-profit schools, and graduate students at 33 - 60% lower rates than non-profit schools. And still, despite all that, students from for-profit institutions graduate with 33 - 50% more loans than students from non-profit despite the fact that the nominal cost of tuition at for-profits is generally far less than that of non-profits. In 2009, the federal government granted some $30B in loans to for-profit colleges, yet half of the students who enrolled in those colleges that year left without a degree or diploma within four months. But it’s hard to get upset about that when you realize that students who graduate from for-profit colleges suffer face higher unemployment rates (27%) than students who drop out (25%). Keep in mind, those rates are nearly three times the national average for college graduates and greater than the unemployment rate for people who have only a high school diploma (23%). You say that higher education needs to be disrupted by for-profit companies; I say that for-profit companies have caused the very problems they claim to solve.

Keep in mind that all this futility is being funded by the federal government. As taxpayers, we should be furious at for-profit educational institutions. The 13% of all higher-ed students who enroll at for-profit schools take out 25% of federal aid dollars and account for 47% of loan defaults. That’s 2 - 3 times the default rate of students enrolled at non-profits.

Despite all this, I do believe that for-profit schools and other forms of alternative education have a significant role to play in our economy. I think this generation’s focus on 4-year degrees to the exclusion of community colleges and vocational programs has been harmful. I also think that not everyone learns best in the traditional academic environment, nor is every subject best taught in lecture format. MOOC, Kahn Academy, and other novel formats offer real promise to a certain segment of learners, and should be encouraged. All that said, I believe that publicly traded educational institutions — and indeed, the idea of building educational companies that generate returns on a venture scale — are generally (but not always) a terrible idea. Educators incentivized by profit aren’t educators so much as manufacturers. In some circumstances (e.g., certain technical trainings), that’s perfectly fine. But in the context of general higher education, the profit motive runs directly counter to the idea of training minds capable of seeing what’s around the bend. To the extent people can find better ways to accomplish that goal, I’m all for it. But that’s not disruption, it’s segmentation and innovation. And if along the way we can retard the profit-seeking “innovators” who’ve led the charge into our latest $500B high-risk credit market, all the better.

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All of the images in this post come from SoberLook.com (@SoberLook), whose anonymous author has been pounding the table about the coming student loan crisis for well over a year now. It’s a treasure of a financial blog.

Fragile Antifragility

To hear Taleb tell it, the state of being antifragile implies an ability to not just absorb shocks, but learn from them. One example he used was air traffic. The explanation goes, when a plane crash occurs, the learnings from that crash are woven into ATC in such a way as to gird the system against further crashes. This ability to learn from mistakes without falling apart can be thought of as antifragility. A counterexample would be the banking system, where the dissolution of one bank reverberates through the system in a nonlinear manner. Before any learnings can be gleaned, the system has failed, and in so doing demonstrated its fragility.

But if these examples are representative, than surely a more intuitive (and clearer) way to distinguish between ‘fragile’ and ‘anti-fragile’ systems would be to describe their internal interdependencies. For instance, nodes within the banking system are generally interdependent—the failure of one will resonate in some way through many other nodes. Within the ATC system, however, the nodes are more or less independent. The failure of one controller generally doesn’t affect or imply the failure of any other controller. To express the point even more simply, the difference between fragility and anti-fragility is the difference between linear and nonlinear (or even complex) systems. 

Oddly, complex systems, while exhibiting incredible robustness and dynamism in the face of shocks, also exhibit intrinsic fragility in the form of cascading failure. A small action can (and often does) lead to an unexpectedly large reaction thanks to linkages beyond our comprehension or control. But yet another set of unseen linkages—perhaps even linkages created in response to the original shock—will generally act to “catch” the system and bring it back to equilibrium. In this light, the distinction between fragility and anti-fragility becomes more a question of timescale than of definition. Is the banking system fragile? In the short term, yes, but in the medium term we’ve managed to bring it back into order, and in the long term we’ve even managed to improve it. Calling something ‘anti-fragile’ then, is just a fancy way of saying two things: first, that a system is complex; and two, that its observer has a short time horizon.

There’s also a definitional issue at play. Taleb steadfastly argues that fragility is an absolute state; otherwise, it could have no opposite. If you were to put the concept of ‘fragility’ on a line, it would exist at -1, ‘resilience’ would sit at 0, and ‘anti fragility’ would come in a +1. The problem with this formulation is that it forces ‘fragile’ into an unnatural box. Fragility is by nature a relative concept—things are fragile only in relation to other objects. Glass may be fragile, but in a world of crystal, we’d probably consider it robust. Put differently, fragility isn’t a state of being so much as a value judgment, and the same goes for robustness. In that frame, anti-fragility is little more than a synonym for robust.

I realize that Taleb attempts to distinguish between things that simply react to shocks from things that actively grow stronger, but I think the distinction fails once you get more than a step or two into the weeds. Whether a system grows stronger or weaker from a shock can’t be known until the nature of the next shock is understood. Stretching a rubber band nearly to its breaking point makes it more robust with respect to subsequent smaller stretches, but also more fragile with respect to subsequent larger stretches. Richard Reid made the TSA more robust to shoe bombers, but more fragile to novel bombing methods (the search for a specific threat often blinds us to unspecified threats). My point is: whether a shock strengthens or weakens a system is less about whether a system adapts than about how it adapts, and one person’s antifragile adaptation is another person’s fragile adaptation. To Richard Reid, the TSA is antifragile. To me, they’re the opposite. So it goes.

Building a Narrative for Premium Publishing

For better or worse, when I think about the future of premium content, this is the narrative I hold in my head. Corrections are always welcome.

——-

The last five years have seen massive investment in display ad technology, and not without reason. Scalable RTB and ad-serving platforms are complicated and costly to build, but they offer advertisers — and by extension, investors — the promise of extraordinarily attractive ROI. And, as usually happens when elevated investment offers elevated returns, a feedback loop emerged. The demand for display ad inventory quickly outstripped supply, creating an attractive imbalance that content farms rushed to address. At the same time, premium publishers rooted in the paper era languished, restrained by legacy cost structures and legacy management. Determined to fight for a dying model, many erected impregnable paywalls, locking away content from those who didn’t value it appropriately. Consumers, with no means of knowing the difference between the bird they held and the unseen bush, reasonably chose the former, and it soon became clear to everyone that premium news was a thing of the past.

But no matter how much we enjoy stories of disruption and death and extinction, the fact remains that the success of one model doesn’t preclude the success of another. Yes, the confluence of a maturing adtech ecosystem and a cost-effective means of generating low-quality content at scale had made content farming into a viable online business model, but that didn’t mean people were no longer willing to pay for premium content. Rather, it meant that when faced with a choice between content known to be good enough and content that might be better, the promise of better wasn’t worth the cost of discovery. Premium publishers had assumed the demand for online content to be as elastic as the demand for offline content, but by locking away their wares, they had deprived consumers of the information necessary to draw a demand curve. The end was in sight.

And then a funny thing happened on the way to the funeral. Obsolete printing, delivery, sales, and subscription operations were pruned away, freeing up capital from overweight balance sheets; senior managers from the Dead Tree era aged out of decision-making roles, replaced by junior colleagues with a completely different set of expectations; and news organizations built for the online world began to emerge in earnest, unencumbered by the costly demands of yesterday’s infrastructures. With available capital, fresh ideas, and a willingness to experiment, the industry has begun to express its needs in the particularized manner entrepreneurs love to hear. Consequently, a stable of startups purpose-built for publisher monetization have emerged (e.g., NewsCred, Visual Revenue, Outbrain, Tinypass, et al.). At the same time, visible and influential publishers such as the NYT and Andrew Sullivan are experimenting with new pricing models. I expect we’ll soon see a world where pricing models run the gamut from purely ad supported to purely paywalled, with infinite native variations in between.

On the consumer side, though, the question remains: will people pay? Can consumer price expectations ever evolve beyond the “zero marginal cost goods should be free” mentality? I believe the answer is yes. Sure, some portion of the population will forever loudly declare that they will never pay for anything, but the beauty of a large market is that you don’t have to capture it all to succeed. Given a sufficiently frictionless buying process and re-anchored expectations of what constitutes a reasonable price, the market can most certainly grow to venture scale. But while the buying process is nearly there (for all the reasons noted above), it’s very hard to judge how far consumer psychology has left to go. If we take purchasing behavior in mobile apps stores as a proxy, there’s ample reason for optimism:

When I think about this data in the context of what’s happened in music and video, I see a pattern wherein the combination of depth of exposure and ease of consumption determines a consumer’s willingness to pay. Pricing, after all, is a relative concept: the more exposure someone has to an industry’s product offerings, the greater their ability to assess each product’s relative value. Eventually, the difference in relative value breaches the penny gap, and it’s all downhill from there. Couple this dynamic with simpler and more available means of distribution, and now you have a formula for turning looters into customers.

On Knowing Your Limits as an Investor

Paradoxically, when “dumb” money acknowledges its limitations, it ceases to be dumb.

- Warren Buffet - 3/1/94

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I.

My definition of investing is relatively simple: the considered wagering of capital on the outcome of events which depend on predictable processes. Not perfectly predictable, of course, but predictable in the sense that a better understanding of the initial conditions (team, market, product, macro environment) or the process (building a company, executing a product plan, gaining distribution) will allow the investor to more accurately forecast the outcome (0x, 1x, 100x). It’s this small but significant dose of determinism that allows us to classify a process as predictable.

The tricky thing, though, is that not every event is predictable; misunderstood conditions, complex processes, and extended timeframes all operate to impair an event’s predictability [1]. In such situations, a better understanding of the process or initial conditions will not (or cannot) lead to better predictions. Indeed, the outcome of is fundamentally uncertain.

If the difference between predictable and uncertain seems muddled, that’s because the distinction depends as much on the observer as on the event. A card counter knows the odds that the next card will be what he needs—his sophisticated understanding of initial conditions makes the draw predictable. A recreational player, on the other hand, knows only what’s in his hand and what’s in front of the dealer—his naive understanding of initial conditions renders the draw uncertain.

I don’t believe in oracles, alchemy, or the Midas touch. But I do believe that sound investment strategy—when guided by an honest and accurate assessment of the world—can deliver outsized returns, even in the presence of true uncertainty.

II.

As an example of how the predictable/uncertain dichotomy can inform an investment strategy, think for a moment about investors with strategies built around network-based businesses.

A number of very smart investors believe they can recognize the initial conditions—that is, the combination of team, market, and product—associated with successful social network breakout before the product has even launched [2]. Implicit in this belief is the idea that a recognizable set of initial conditions will reliably lead to positive outcomes—in other words, that the process of network creation is fundamentally predictable. That networks can be predicted from initial conditions is a tenant of the early stage community, though it’s generally discussed in terms of “high risk/high reward” [3]. If you’re able to reliably recognize these “risk/reward” conditions, then building a concentrated book of such companies can make strategic sense [4].

But what if you don’t believe that you’re one of the gifted few? What if to you the process of network breakout appears, for all intents and purposes, random? This is actually the view I take, that I simply lack the tools to predict breakout networks from a standing start. But that doesn’t mean I can’t invest in network-based businesses, it just means that I need to build an investment strategy that accounts for my shortcomings. Failing that, any money I put into a network business would not be an investment so much as a gamble. And the two are most definitely not the same.

So what would a reasonable strategy for me look like? Well, while I don’t believe in my ability to predict breakout from initial conditions, I do believe in my ability to identify the early indications of breakout. If the expected return from a breakout network offsets the price increase associated with more observation, then I could build a rational strategy around that ability: rather than trying to get in early, I could tailor my brand and my terms to appeal to companies approaching takeoff.

But what if I don’t have the capital reserves necessary to pursue this strategy? Maybe I’m a new investor without a track record and breakout companies don’t want me in their syndicates. In that case, I could run the numbers and determine that the returns from a successful network are so large, and the odds of randomly investing in one of such a probability, that a rational strategy for me would involve investing in every pre-launch network I could find. I could create a reputation as the “network angel” and leverage it for access, knowing that, though my capital will be spread thin, the economics of my portfolio will tilt in my favor.

III.

The point of all this is uncontroversial, that an investor’s strategy should account for their limitations. Unfortunately, just because we know what to do doesn’t mean we actually do it. A well-defined strategy is only as good as its execution, and even the best investors will be tempted to ignore the rules they have so assiduously defined. That is why we form strategy: to help us remember that what we see is not all there is. In the moment, it’s easy to forget our own rules, or to convince ourselves that this is a special case, or that this special case, if we squint, actually does fit into our box. In such moments, what happens next isn’t an investment so much as a gamble. And the insidious thing is that when we win these gambles, the temptation to place more only grows stronger. If “this time is different” is the most dangerous thought in finance, then “I knew it in my gut” must be number two.

There are investors out there—though fewer than you might think—who possess the self-awareness and discipline to build and execute sound strategy. Holding to a disprovable hypothesis until it’s either been disproven or run its course is monumentally difficult, if for no other reason than it sounds so deceptively simple.

When I think about the qualities I’d want in someone running my money, my mind doesn’t go to intelligence or confidence or even a track record; rather, I want someone who knows their limits and sees them for what they are: barriers to be identified, understood, and accounted for within the context of a larger strategy. Steve Jobs can have the wild ones. When it comes to my money, I’ll take the self-limiters every time.

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Market Design for Dummies

For those who yearn to understand basic market theory but could never wake up in time for Macro (don’t all stand up at once!), I present my two favorite short papers on the subject:

• The Economic Organisation of a P.O.W. Camp
• Monetary Theory and the Great Capitol Hill Baby-sitting Co-op Crisis

Hopefully, the titles are self-recommending.