What’s a mid-career software engineer actually worth? Try $779,000 per year as a lower bound.

Currently, people who either have bad intentions or a lack of knowledge are claiming that software engineer salaries are “ridiculous”. Now, I’ll readily admit that programmers are, relative the general population, quite well paid. I’m not about to complain about the money I make; I’m doing quite well, in a time and society where many people aren’t. The software industry has many problems, but low pay for engineers (at least, for junior and mid-career engineers; senior engineers are underpaid but that’s an issue for another time) doesn’t crack the top 5. Software engineers are underpaid, relative to the massive amount of value (if given proper projects, rather than mismanaged as is typical) they are capable of delivering. In comparison to the rest of the society, they do quite well.

So what should a software engineer be paid? There’s a wide disparity in skill level, so it’s hard to say. I’m going to focus on a competent, mid-career engineer. This is someone between 5 and 10 years of experience, with continual investment in skill, and probably around 1.6 on this software engineering scale. He’s not a hack or the stereotypical “5:01″ programmer who stopped learning new skills at 24, but he’s not a celebrity either. He’s good and persistent and experienced, but probably not an expert. In the late 1990s, that person was just cracking into six-figure territory: $100,000 per year. No one thought that number was “ridiculous”. Adjusted for inflation, that’s $142,300 per year today. That’s probably not far off what an engineer at that level actually makes, at least in New York and the Bay Area.

Software engineers look “ridiculous” to people who haven’t been software engineers in 20 years (or ever) and whose numbers are way out of date. If you’re a Baby Boomer whose last line of code was in 1985, you’re probably still thinking that $60,000 is a princely sum for a programmer to earn. When one factors inflation into the equation, programmer salaries are only “at record high” because inflation is an exponential process. Taking that out, they’re right about where history says they should be.

I would argue, even, that programmer salaries are low when taking a historical perspective. The trend is flat, adjusting for inflation, but the jobs are worse. Thirty years ago, programming was an R&D job. Programmers had a lot of autonomy: the kind of autonomy that it takes if one is going to invent C or Unix or the Internet or a new neural network architecture. Programmers controlled how they worked and what they worked on, and either answered to other programmers or to well-read scientists, rather than anti-intellectual businessmen who regard them as cost centers. Historically, companies sincerely committed to their employees’ careers and training. You didn’t have to change jobs every 2 years just to keep getting good projects and stay employable. The nature of the programming job, over the past couple decades, has become more stressful (open-plan offices) and careers have become shorter (ageism). Job volatility (unexpected layoffs and, even, phony “performance-based” firings in lieu of proper layoffs, in order to skimp on severance because that’s “the startup way”) has increased. With all the negatives associated with a programming job in 2014, that just didn’t exist in the 1970s to ’80s, flat performance on the salary curve is disappointing. Finally, salaries in the Bay Area and New York have kept abreast of general inflation, but the costs of living have skyrocketed in those “star cities”, while the economies of the still-affordable second-tier cities have declined. In the 1980s and ’90s, there were more locations in which a person could have a proper career, and that kept housing prices down. In 2014, that $142,000 doesn’t even enable one to buy a house in a place where there are jobs.

All of those factors are subjective, however, so I’ll discard them. We have sufficient data to know that $142,000 for a mid-career programmer is not ridiculous. It’s a lower bound for the business value of a software engineer (in 1999); we know that employers did pay that; they might have been willing to pay more. This information already gives us victory over the assclowns claiming that software engineer salaries are “ridiculous” right now.

Now, I’ll take it a step further and introduce Yannis’s Law: programmer productivity doubles every 6 years. Is it true? I would say that the answer is a resounding “yes”. For sure, there are plenty of mediocre programmers writing buggy, slow websites and abusing Javascript in truly awful ways. On the other hand, there is more recourse for a good programmer who find quality; rather than commit to commercial software, she can peruse the open-source world. There’s no evidence for a broad-based decline in programmer ability over the years. It’s also easy to claim that the software career “isn’t fun anymore” because so much time is spent gluing existing components together, and accounting for failures of legacy systems. I don’t think these gripes are new, and I think tools are improving, and a 12% per year rate sounds about right. Put another way, one who programs exactly as was done in 1999 is only about 18 percent as productive as one using modern tools. And yet that programmer, only 18% as productive as his counterpart today, was worth $142,000 (2014 dollars) back then!

Does this mean that we should throw old tools away (and older programmers under the bus)? Absolutely not. On the contrary, it’s the ability to stand on the shoulders of giants that makes us able to grow (as a class) at such a rate. Improved tools and accumulated knowledge deliver exponential value, but there’s a lot of knowledge that is rarely learned except over a decades-long career. Most fresh Stanford PhDs wouldn’t be able to implement a performant, scalable support vector machine from scratch, although they could recite the theory behind one. Your gray-haired badasses would be rusty on the theory but, with a quick refresh, stand a much greater chance of building it righjt. Moreover, the best old ideas tend to recur and long-standing familiarity is an advantage. The most exciting new programming language right now is Clojure, a Lisp that runs on the Java Virtual Machine. Lisp, as an idea, is over 50 years old. And Clojure simply couldn’t have been designed by a 25-year-old in Palo Alto. For programmers, the general trend is a 12% increase in productivity; but individuals can reliably do 30 percent or more, and for periods spanning over decades.

If the business value of a mid-level programmer in 1999 was $142,000 in today’s dollars, then one can argue that today, with programmers 5.7 times more productive, the true value is $779,000 per year at minimum. It might be more. For the highly competent and for more senior programmers, it certainly is higher. And here’s another thing: investors and managers and VPs of marketing didn’t create that surplus. We did. We are almost 6 times as productive as we were in the 1990s not because they got better at their jobs (they haven’t) but because we built the tools to make ourselves (and our successors) better at what we do. By rights, it’s ours.

Is it reasonable, or realistic, to argue that mid-career software engineers ought to be earning close to a million dollars per year? Probably not. It seems to be inevitable, and also better for society, that productivity gains are shared. We ought to meet in the middle. That we don’t capture all of the value we create is a good thing. It would be awful, for example, if sending an email cost as much as sending a letter by post or, worse yet, as much as using the 19th-century Pony Express, because the producers of progress had captured all of the value for themselves. So, although that $779,000 figure adequately represents the value of a decent mid-career engineer to the business, I wouldn’t go so far as to claim that we “deserve” to be paid that much. Most of us would ecstatic with real equity (not that 0.05% VC-istan bullshit) and a quarter of that number– and with the autonomy to deliver that kind of value.

Inverted placism: a possible future in which Silicon Valley’s a ghetto

I was having brunch with a couple of friends who are lawyers, and we were talking about desirable and undesirable places to live. Seattle (where I may be moving in early 2015) scored high on every list, and one of the attorneys said something to the effect of, “I’d love to live there, but it’s next to impossible to get a job there.” Getting a law job in Seattle is, apparently, ridiculously difficult. This surprised me, because law is even more pedigree-obsessed than VC-funded technology, and where there’s pedigree obsession, there’s placism. Placism, for law, seemed to favor New York and D.C. to the exclusion of all else. There were some attorneys making lots of money in entertainment law (or as divorce lawyers) in Los Angeles, but it wasn’t prestigious to be in a “secondary” market. That seems to be changing, with locations like Seattle and Austin– desirable places to live, no doubt, but not law hubs– becoming very selective, and some moreso than New York.

Ten years ago, in large-firm corporate law (“biglaw”) New York was the place where attorneys wanted to stay as long as they could. Even though the pay wasn’t substantially higher– when adjusted for cost of living, it was invariably lower– the prestige was strong and followed a person for life. The best outcome was to make partner in one’s New York firm. Perhaps 1 in 10 was offered the brass ring of partnership. The next set, those who were clearly good but wouldn’t get partnerships, would move to firms in “secondary markets” and become partners out there. It was acceptable to move out to Austin or L.A. or Seattle, in your mid-30s, if Manhattan partnership wasn’t in the cards, but few planned for it. Law is even more pedigree-obsessed than VC-funded technology, and so placism is pretty major, and the going assumption has, for a long time, been that the best students of the top law schools will invariably end up in New York.

It seems to be changing. More attorneys are considering New York their backup choice, not wanting to put up with the long-hours culture and high rents. It’s no longer considered unusual for top talent to favor other locations, and some of those smaller markets are developing a reputation for being much more selective than New York, the old first choice.

Does anyone care to guess how this might apply to technology?

Silicon Valley isn’t stable

Balaji Srinivasan gave a talk at Y Combinator’s Startup School entitled “Silicon Valley’s Ultimate Exit”. In it, he decried the four traditional urban centers of the United States: New York, Boston, Los Angeles, and Washington, DC. He named that stretch “the Paper Belt”, a 21st-century analogue of the “Rust Belt”. See, all of those cities are apparently run by dinosaurs. Boston is the academic capital, but MOOCs are rendering in-person education obsolete. D.C. is apparently no longer relevant, under the theory that the decline of nation states (which will occur over the next 200 or so years) might as well be concluded to have already happened. New York? All that media stuff’s being replaced by the Internets. Los Angeles? Well, Youtube and iTunes and Netflix have already disrupticated Hollywood, which might as well be relegated to history’s dustbin as well (except for the fact that someone still has to make the content).

Silicon Valley’s arrogance is irritating and insulting. I’m not exactly lacking when it comes to intellectual ability, and on several occasions, I’ve interviewed for a position in the Valley (for the right job, I’ll work anywhere). On multiple occasions it has happened this way: I knock the code sample out of the park, on one occasion submitting one’s considered one of the 3 best submissions. I nail the technical interview. I get the offer… and it’s a junior position because, whatever I accomplished up to this point, I didn’t do it in California. The effect of placism is very real in technology, and it’s strongest in the Valley.

I don’t see this elsewhere. Banks and hedge funds don’t care if you’re from a rural village in China. If you’re smart, they respect it. They have the intellectual firepower to recognize intelligence. What about the Valley? Surely, I’m not saying that the people in the Valley are dumber? Well, it’s not quite that. As individuals, I don’t think there’s a difference. There are A-level intellects everywhere, whether you’re in the middle of Nebraska or in the Valley or on Wall Street. The problem, instead, is that the Valley has a passive-aggressive consensus culture, which means you need to impress several people to get the green light. In New York, it’s typical for an influential person to say, “I like this guy, and those who don’t won’t have to deal with him, but I think he’s fucking brilliant”. In California, that doesn’t happen. This gives intellectual mediocrities (who can, likewise, be found in Valley startups and on Wall Street) a certain show-stopping power (“I don’t think he’s a team player”, “she’s not a culture fit”) that they don’t have, to the same extent, on the East Coast.

For traders and quants, pedigree isn’t all that important. It can get you in the door, but it ceases to matter after that. In the Valley, pedigree matters much more, because recognizing individual excellence challenges the “collaborative culture” and the “laid back” mentality that California is “supposed” to have and, if you can’t bring up a person’s individual firepower, you start defaulting to credentialism and prestige. Not all Stanford grads are geniuses (see: Lucas Duplan). On the East Coast, it’s socially acceptable to say, “He’s fucking stupid and I’m sure his parents bought him in.” That’s a pretty clear “no hire”. In California, you can’t say that! Instead, in the California culture, you end up having to say something like, “Well, his problem-solving skills aren’t what I expected, and I think he’d be unhappy in a technical role, but I guess we can give him a product position and tap the Stanford network.” To me, that’s a “no hire” but, to many managers, that sounds like a ‘yes’.

I had the reverse of this experience when (as part of my consulting practice) I was hiring an engineer for a startup. He was a 20-year-old college graduate, sharp as fuck and probably a better programmer than I was, but socially inept. I said, “he’s brilliant, but would need some mentoring on the social aspect of the work”. To make it clear, I was being as honest as I could be, and my recommendation was to hire him. Unfortunately, my “he’ll need social mentoring” was taken as a passive-aggressive way of saying “no-hire”, rather than a completely honest acknowledgement that a good candidate had (minor) imperfections. He wouldn’t have been hired anyway, nor would he have liked the place, so it didn’t matter in the end. Still, it shocked me that such a minor note against someone (I said, “he’ll need social mentoring”, not “he’s an incorrigible fuckup”) could be taken so far out of proportion.

The point of this digression is that, because people in the Valley refuse to communicate meaningfully, and because of the consensus-driven culture, the rank-ordering of potential candidates that is actually used is the one already furnished. For younger candidates, that’s derived from educational pedigree. For older ones, it comes down to job titles and companies to some small degree, but much more important is location. Placism rules in the Valley.

There’s nothing stable about that, in my view. Academic institutions have lifelong contracts (tenure) with professors and gigantic capital investments, so universities tend to stay put. (Universities that are too prolific with branch campuses, such as NYU having an Abu Dhabi campus, destroy whatever prestige they might otherwise have.) The seat of the U.S. government is, likewise, unlikely to leave D.C. except in event of an unforeseen catastrophe. Hollywood’s geographical advantage (its proximity to a diverse array of terrain types) is still major, because the cost of travel with a film production team is extremely high. New York? New York won’t lose finance (the exchanges are there) and, even if it did, it would still be New York.

That’s something that the Valley, with its arrogant placism, doesn’t get. Let’s say that New York’s financial industry takes a catastrophic dive. We see apartments once valued at $50 million selling for $4 million, and rents dropping to Midwestern levels. And then? Creative people will move in, rapidly, and restore life to the city. New York isn’t beholden to one industry. It will always be New Fucking York. Unless we see a recurrence the 1970s general abandonment of cities by the American population (and, in my lifetime, we probably won’t) the worst-case scenario for it is that it becomes like Chicago: an also-ran city that is, in spite of its lack of “paper belt” specialty, thriving and an excellent place to live.

New York can lose its status as the prestige center of biglaw. It could even lose Wall Street. (That would be a disaster for New York property owners, but the city itself would be resilient.) Silicon Valley, on the other hand, is fucked if the placism of venture-funded technology inverts. That just might happen, too.

Inversion of placism tends to happen when the young and creative decide that the advantages of living in the “prestigious” place are not worth the disadvantages. The rents are too high, the culture is too elitist, and upward mobility is too low. The progeny of well-connected families still end up in the prestigious place (New York biglaw, Valley technology) but the successes of the next generation head elsewhere. Sometimes, they choose another location; others, there’s a sense of diaspora for a while. The Valley could easily lose its singularity. It’s not a great place to live (it’s a strip mall) and it’s far too expensive for what it offers. In truth, everything about it is mediocre except, to some extent, the work; but 95 percent of the work is mediocre (who wants to work in operations at IUsedThisToilet.com?) and getting the other 5 percent requires an advanced degree from a top-5 CS department, or elite connections. A few good people have those and will be able to stomach the Valley, but most good people come from no-name schools (not because the no-name schools are better, but because most people come from no-name schools) and don’t know anyone important out there. In 2010, talented and naive 22-year-olds were willing to move out to the Valley and provide cheap, clueless, highly dedicated labor under the naive (and wrong) assumption that a year at a startup would have them personally introduced to Peter Thiel by the founders. Is that trickery going to work in 2016? I doubt it.

Starting about now, it’s going to become increasingly evident that the talent wants to be outside of Silicon Valley if the same quality of job is available elsewhere. In fact, being in Silicon Valley after 35 will mean one of two things: astronomical success, or dismal mediocrity, with no middle ground. Being in California, at that age, and not being part of the investor community (either as a VC, or as a founder able to raise money on a phone call) will be a mark of shame. If you’re good, you should be able to move to Seattle or Austin, no later than 30, and get the same quality of job. If you’re really good, you can get that kind of job in St. Louis or Nashville. Aside from the outlier successes ($20 million net worth, national reputation) who can make a Silicon Valley residence part of their personal brand, it’ll be the mediocrities who stick around the Valley, still trying to catch a break while ignoring the hints that have been dropped all around them.

By 2020, this will have more of a “diaspora” shape. There won’t be a new tech hub yet. You’ll see talent gravitating toward places like Seattle, Boulder, Portland, Chicago, Austin, Pittsburgh, and Minneapolis, with no clear winner. Millennials are, if not blindly optimistic, attracted to the idea of turning a second-rate city into a first-rate one. By the late 2020s, it will be clear whether (a) new hubs have emerged or (b) technology has become “post-placist”. I’m not going to try to opine on how that will play out. I don’t think anyone can predict it.

Cheap votes

What gives Silicon Valley its current grip on technology? The answer is a concept that seems to recur when aggregations such as democratic elections and markets break down. Cheap votes.

Electoral voting, statistically, can actually magnify the power of a small number of votes. If there are 101 voters and we model 100 votes as coin-flips, the power of the 101st vote isn’t a 1-in-101 chance of swaying the election. It’s about 1-in-13. (Due to the central tendency of the mean, there’s a 7.9% chance that the 100 votes split evenly.) Likewise, the statistical power of a voting bloc increases as the square of its size (in the same as the variance of perfectly correlated identical  variables, when summed, grows as the square of the individual’s variance). What this means is that a small number of voters, acting as a bloc, can have immense power.

Another issue is that many voters don’t really give a damn. Low voter turnout is cited as a negative, but I think it’s a good thing. Disinterested people shouldn’t vote, because all they’ll do is add noise. The ugly side effect of this is that societies generate a pool of cheap votes. Ethical reservations aside, there are plenty of people who care so little about electoral politics that (absent a secret ballot) they’d sell their vote for $100. How much is a vote worth? To the individual, the vote is worth less than $100. But, to many entrenched interests, 500,000 votes (which can sway a national election) is worth a lot more than $50 million.

When you allow vote-buying, power shifts to those who can bundle cheap votes together. That’s obviously a very bad thing for society. Such people tend, historically, to be deeply associated with society’s criminal elements, and corruption ensues. This is one of the major reasons why the secret ballot is so important. Anonymity and privacy in voting are sacred rights, for sure, but we also want to kill the secondary market for cheap votes. There’s no real harm in someone selling his vote to his grandma for $100, but if we allow vote-buying to take place, we give power to some unelected, vicious people who use the statistics of electoral practices to subvert democracy.

Markets are the voting systems of capital allocation and business formation, designed as principled plutocracy rather than a democracy. Of course, just as in democracies, there are a lot of cheap votes to go around. Plenty of middle-class people want to park their savings “somewhere” and watch their numbers go up at a reasonable annual rate, but have no interest in dictating how the sausage is made. They don’t know what the best thing to do with their $500,000 life savings is and, to their credit, they admit as much. So they put that money in bonds or index funds and forget about it. Some of that money ends up in high-risk, high-yield (in theory) venture capital funds.

VCs are the cheap vote packagers of a certain 21st-century question: how do we build out the next stage of capitalism, which requires engagement and autonomy within the labor pool itself to a degree that disadvantages giant organizations? The era of large corporations is ending. It’s not like these companies will disappear overnight, or even in 50 years, but we’re seeing a return to small-scale, niche-player capitalism in which a few small companies manage to have outlier success and (if they want it) can become large ones. VC is the process of taking cheap votes (passive capital) and attempting to influence the formation processes of the nation’s most innovative (again, at least in theory) small businesses.

Abstractly, your typical doctor in St. Louis would rather have more small businesses in the Midwest (his children need jobs, and they may not want to move to Mountain View) than in California, and might prefer his capital being deployed locally. But he has a full-time job and is smart enough to know that he’s not ready to manage that investment actively. So, he parks his money in an “investment vehicle” that has the funds redirected to a bunch of careerists in California who care far more about the prestige of association with news-making businesses (hence, the focus on gigantic exits) than the success of their portfolios. His returns on the investment are mediocre, but his locale is also starved of passive capital, which has all been swept away into the bipolar vortex of Sand Hill Road.

Passive investors don’t care, enough, to pull their funding. In fact, it’s rarely individual investors whose capital ends up directly in venture capital. Because of protections (which may not be well-structured, but that’s another debate) that prevent middle-class individuals from investing directly in high-risk vehicles, it’s actually large pension funds and university endowments (increasing the indirection) that tend to end up in VC coffers. With all this indirection, it’s not surprising that passive investors would tacitly accept the current arrangement, which congests Northern California while starving the rest of the country. But is this arrangement stable? I think not. I think that, while it takes time, people eventually wake up. When it happens, San Francisco may still possess its urban charm, but the Valley itself is properly screwed.

Gervais / MacLeod 26: r- and K-selection in organizations and capitalism.

I wrote about the three existing varieties of capitalism last month. Now I’m going to focus on a concept from evolutionary ecology, which is the r/K selection theory, pertaining to the tradeoff between quality and quantity in reproduction, after which I will detail its connection to business.

First, I should focus on the pure ecology. An r-selected organism (r-strategist) is one that has lots of offspring, but most do not survive. For example, a spider that leaves a brood of hundreds of infants, most of which will not live even a day, is an r-strategist. On the other hand, whales and humans, with their long gestation periods and high parental investment, are much closer to being pure K-strategists: few offspring, high quality. In reality, almost none of the higher animals clearly fall into one category or the other: mammals, perhaps notoriously, show adeptness at both r- and K-selective sexual strategies, with males and younger animals more r-driven and females and older animals more K-favoring. For an animal to have both impulses is most adaptive to nature because the two drives serve different purposes. After a population crash or calamity, the r-selectors will repopulate the quickest; when the population is near or at capacity, however, K-selectors have the advantage. Since both classes of circumstance have occurred over the millions of years in which complex life evolved, both strategies are part of our heritage.

One thought, that I’ve always thought credible, is that the emergence of high intelligence in mammals is, at least in part, an artifact of an arms race between two opposing sexual drives. Humans clearly have both r- and K-strategic sex drives, and much of the narrative of morality as civilizations have evolved is about the tension between these two. The r-selective sex drive (Freud’s id) just wants to spread seed as far as it can go; a hundred offspring would be desirable, a thousand is even better. The harem-holding kings and pharaohs of antiquity were notorious r-strategists. On the other hand, the K-strategist invests highly in a small number of offspring (in ancient times, “a small number” might have been 5-10). K-strategists tend toward monogamy: if one is optimizing for quality of offspring, the best strategy is to find one high-quality partner and treat that person and the children well. K-selectors were the first monogamists, but also civilization’s first architects. An r-strategist doesn’t care about social stability, because the general assumption is that with a few hundred offspring, some will thrive no matter how damaged the environment becomes. K-strategists, on the other hand, want social progress because a fair, reasonable, predictable, and progressively improving society is the one in which quality offspring have the best chances. (Damaged environments are much more random in what they reward and punish.) Finally, the first feminists (male and female) were K-strategists, which is one of the reasons why Sex and the City pseudofeminism is so clearly inconsistent and laughable. Monogamy, democracy, feminism, and social justice all come from our K-selective halves; the r-strategist within us doesn’t give a damn about any of that.

Most religions portray the more ancient r-drive as “evil” (or, at least, sinful) and the K-drive as “good”. I don’t think it’s useful to categorize nature that way. We are animals, and what makes good or evil has more to do with our ability to reflect on, and filter, our impulses than what they are or where they come from. When the species is threatened and the environment is damaged, it’s r-selectors who help it bounce back. If nine-tenths of the male population were killed in a pandemic, moral reservations about polygamy would vanish as humanity struggled to recover. On the other hand, a stable society views the impulsive, present-focused r-selectors as a parasitic nuisance, and people who are extreme on the r/K spectrum are given the name, “psychopath”, because they fit so poorly into a cooperative, peacetime society. 

Memes!

In the world of biology, the delineation between r- and K-selection is not always clear. Trees, for example, distribute lots of seeds (r-strategic) but the adult organism is an extreme K-strategist– designed to live for centuries. Every organism would prefer, of course, to optimize quality and quantity both– it’s only when they trade off against each other that we can discern an specific character– and it’s not clear that specific species favored one or the other, with most advanced species relying on both. In the world of memes, however, we find that there’s much more separability. A biological organism is a complex beast (literally) formed by r- and K-pressures over millions of years; but memes are simpler, and it’s usually easy to tell an r-meme from a K-meme. They are utterly different in character. Many memes are formed consciously, and some are built to grow rapidly (“go viral”) and become fads, then pass away. Others are designed for longevity and cultural impact. The Wire and Mad Men are TV shows that were clearly not built to achieve the highest ratings; they were built for quality and longevity, and people will still want to watch them five decades from now.

Much of what we do in civilization is reproduction of ideas. Laws form as people replicate what they consider to be the fairest means of dispute resolution, rehabilitation, protection of society, and (when needed) punishment. Religion and culture emerge to reproduce successful ideas, and r- vs. K-selective tensions are evident there. Some religions want to grow in membership and are willing to tolerate dilution of the message (r-selection). Others grow conservatively and work aggressively to prevent dilution or evolution in the core ideas (K-selection). Then there’s business. Guild systems are K-selective, insofar as they attempt to mandate quality in the trade (admissions, market manipulation to prevent “race to the bottom” dynamics) at the expense of growth. For example, the advisory “bottleneck” in graduate schooling (a gestation period) is a K-strategic invention. However, these systems tend to be brittle in the face of a competitive global market.

Without explicit manipulations and traditions, business tends toward radical r-selective behavior. A manager might give 15 reports, making it impossible for them to be adequately mentored. New hires are not treated as valuable initiates to a guild or tradition (and possibly put through a painful but controlled “hazing” process) but, rather, treated as utterly marginal members of zero credibility and value. Being hired into the company means nothing; they have to prove themselves once there. Most companies, despite saying otherwise, would rather grow rapidly and dominate a market, at the expense of personnel quality and mismanagement, than grow at a controlled rate and risk second-rate status in the market.

What’s right?

While the impulse to associate K-strategies with “good” and r-selection as “bad” is strong in civilized people, we have to avoid that, especially when analyzing business and technology. There’s no clear right or wrong. Consolidation, obsolescence, declining reputation, and adverse market changes can kill off small players (K-capitalists) of even the highest quality, meaning that the “get big and moralize later” mentality of the r-capitalist is sometimes the only thing that works. Additionally, it’s not clear to me that business K-strategists, as a class, have any real moral high ground. I have plenty of friends working in non-profit organizations (professed K-strategic companies) and the politics in them are just as vicious and mean-spirited as in for-profit corporations– sometimes worse, because of resource scarcity (cf. academia). If I’m going to be a subordinate, I’d rather work for someone “greedy” (r-selective) who will promote smart people who make him money (creating some semblance of meritocracy) than a “visionary” (K-strategist) driven by ego alone.

All that said, there is something dissatisfying about the dominant r-capitalism of the modern corporate world. Organizations that exist only to grow their own shares of society’s resources are not inspiring places to work. No one who is 22 wants to be “working for money” at age 50, or even 30. One knows, every day, in such an organization that it will do whatever it sees as being in its best interest, including terminating one’s employment on whatever terms favor it most. The r-selective corporation feels cold and soulless and, in fact, it is that by design. Its only purpose is to replicate a set of known business processes– admitting declines in quality due to environmental heterogeneity, resource issues, and regression to the mean in the quality of involved people– as far as at least one iota more is gained than spent. It’s a machine making copies of copies of copies that doesn’t stop copying copies until the last replica is utter junk– worth less than the material used to make it. By that point, most of what it has produced is of near-zero value, and since undifferentiated stuff (e.g. garbage, unwanted possessions, pollution) is of negative value, this often means harm will be inflicted on the world if externalities are present.

Nonprofit organizations, in theory, are K-strategic organisms. They serve a social purpose other than the profit motive. The problem with them is that they tend to operate in ways that are out of tune with what the market will reward (or, more bluntly, most are losing money). In business, K-strategic work is often thankless: the rewards come back in the long term, and are mostly enjoyed by other people. We see this in the macroscopic sense (R&D work often pays off a hundredfold or more to society, but the sponsor is likely to break even in terms of value captured) but it’s also true in the microscopic world of personal ethics. The r-operating manager gets credit for the work of 15 people; the K-strategic mentor improves the employability of her charges, relying on their loyalty to enjoy any gains. The problem with the non-profits is that they’re often so economically marginal and dependent that these constant stresses push them toward r-selective behavior (e.g. “publish or perish” in the university industry) on the inside. The meltdown of academia (both in terms of its job market, and in terms of the quality of what’s been produced over the past thirty years) is a case study in this. Existential threats force any organism toward r-strategic behavior, and nonprofit organizations are no different.

So far, we’ve looked at large corporations that are inherently r-selective, and the nonprofit world, in which daily financial pressures can create an r-selective flavor in spite of K-oriented macroscopics. What about small businesses? Unfortunately, most of those are cash-strapped as well, fighting constantly for survival.

Then there are Silicon Valley startups, the worst culprits in terms of inconsistency between a K-selective message (“change the world”) and r-strategic behavior. These young, brash companies have done a great job of marketing themselves as an alternative to Corporate America when their actual purpose is either (a) to become one of those corporate beasts in record time, or (b) assimilate into the existing phalanx on favorable terms for the founders. In fact, they’re far from an antidote to the negatives of r-strategic corporate capitalism; with venture capitalists as their real executives, they’ve managed to copy many of its worst attributes and behaviors.

Is there K-selective capitalism?

Now, let’s study the 3 dominant capitalisms from an r/K-selection approach. Supercapitalism and corporate capitalism are both r-selective, winner-take-all, sharp-elbows ecosystems, and unapologetic about being that way. This is also why the “faceless” corporations and equally faceless manipulators behind supercapitalism get such a negative name. Terms like “global elite” and “multinational corporation” underscore the parasitic and anti-patriotic nature of these powerful but disfavored capitalisms (reviled by both left and right, tacitly embraced by the meek center) that have no community or homeland, and generally harm (exploit) their neighborhood, then pull up stakes and move to another one.

An r-selective corporation doesn’t stand for anything, but it often provides– and often more importantly, distributes– a commodity with incredible efficiency. U.S. Steel and Standard Oil didn’t have missions– neither does Exxon-Mobil or Bank of America– but they served up their respective commodities so well as to become indispensable to society. In an r-selective firm’s maturity, it is best thought-of as a utility. The innovations are done, rewards for risk-taking and creative thinking have been distributed, and the only rewards left are those for office social climbing (which can be done in a capitalistic or governmental bureaucracy). If one firm dominates the space, as often occurs in a natural monopoly, there will be pressure to nationalize it, which is an assertion that, although the process, now well-studied, may be extremely valuable, the capitalistic enterprise surrounding it has ceased to be of value. Government is preferred by many societies to handle such utilities because, while some governments fail at doing so, governments can be K-strategic in behavior (purposes like social justice, widespread education, infrastructural quality and stability are K-selective aims at which governments have seen some victories– as well as hideous defeats). In fact, it might be only governments that can operate in a K-selective way at a certain scale (but I tend to doubt that).

Broadly speaking, war favors r-selectors while peace favors K-selection. When there is stress and turmoil, that which can replicate quickly tends to fill gaps first. Qualitative refinements tend to win out amid stability and prosperity, when the produce of the r-strategist is viewed as unwanted congestion; but scarcity creates an environment in which r-players thrive. The issue in the broader world, however, is that r-strategists can easily make war if they wish. War and distrust seem to be more entropic states that are, quite often, easier to bring into existence. It is not clear whether it’s best for business to be in war or peace, but the r-selective firm has a clear advantage over its K-selective rival, which is that it’s (at least from theoretical first principles) easier to create the warlike environment in which the r-strategist has the advantage. K-selective capitalism seems rare, and perhaps unfit, and that might explain why it is practically never found at scale.

Yeoman capitalism, for its part, certainly tries to be K-selective. The yeoman capitalist enters business for the challenge, to improve her lifestyle, to give something back to the community, or because she genuinely enjoys the work. While the r-strategic capitalist wants to gets as much work out of people for as little as possible, the K-strategist genuinely wants to improve the lives of her customers and employees, and to provide a service or product of superior (even artistic) quality. The issue that these yeoman capitalists face, however, is that they’re often under perennial existential threat. Larger, r-strategic enterprises might descend and make war.

A possible 4th capitalism?

Each of the three capitalisms that I described has shortcomings. I’ll attempt to explain why a fourth alternative might be forming in the technology industry. Let’s look at each from whether it is local or global, and also whether it’s r- or K-selective.

Corporate capitalism is global and r-selective. It is global insofar as the corporation will attempt to engage with any market, employ people at every talent level, and exploit every resource to which it can gain profitable access. It is the only capitalism of the three that can provide anything resembling universal employment, because it will find a place for everyone, excluding (for a moment) the matter of a minimum wage. If illegal enterprises (such as drug empires) are included, it finds positions (as street-level drug dealers, perhaps the worst job ever due to its pathetic pay and extreme danger) even for the otherwise unemployable.

Supercapitalism is local and r-selective. Its aims are power, fast money, rapid growth and proliferation, but it only wants to employ the elite. Investment banks and VC-funded startups take nothing lower than the upper-middle class. Prestige and reputation are too valued by the supercapitalist, who relies on those assets for maximal access to manipulation, for such a person to mingle with the larger and dirtier (and far less prestigious) world. A supercapitalist will exploit an opportunity as far as it can go, so long as personal reputation remains intact. That emasculating insistence on reputation management renders the supercapitalist’s reach limited and furtive; thus, supercapitalism will never be able to dominate daily life as corporate capitalism, in the U.S., has from about 1920 to 2025(?).

Finally, yeoman capitalism is K-selective but local. The yeoman capitalist has a vision and purpose other than making money (because most yeoman capitalists would make more money, and certainly enjoy more security, if they closed shop and got regular jobs). These lifestyle businesses don’t scale– they’re not built for that– and the unfortunate side effect is that they’re always exposed enough to corporate intrusion that freedom from immediate existential threat (as enjoyed by established companies) never occurs. Yeoman capitalism will never go beyond the local scope because it relies both on high talent and starting capital, and the percentage of people who have either is already small, but the proportion who have both is tiny.

Can there be a more globally oriented K-selective capitalism? Right now, the global and K-selective mentality has been the province of governments and nonprofits. I think that there’s quite a large need set (especially pertaining to peoples’ aesthetic and creative needs) to which both have failed. It might be where each sits with regard to short-term risk; nonprofits are too constantly under the gun– constantly raising money– while governments tend to be cushy, established, and legacy-bound. There might be a happy middling ground between those two cases into which a highly innovative but K-strategic capitalism (focused on quality of innovation) could move.

Technology, however, is starting to see changes that result from talent’s leverage (relative to property; as there are really only two important commodities in the world, property and talent, also known as past and future) reaching a historic high. Companies like Valve and Github are experimenting with low-overhead management styles and open allocation. Companies are gradually figuring out that it’s impossible to truly employ top talent; you sponsor it for a while. If you invest in a talented person’s career– and the open-allocation strategy is to hire mature self-starters already capable of investing in their own career (and kicking back things of value to the firm) given full autonomy– you might get that person to stick around for 5 to 10 years. If you don’t, the Dead Sea Effect will set in rapidly.

Except in R&D labs with high levels of autonomy (e.g. Xerox PARC, Bell Labs) there was just too much difference in bearing between top talent and corporate life for them ever to get along. Corporations can’t hold the best people unless they give them (a) 20-percent annual increases, which is expensive, (b) extremely high levels of autonomy, which can be politically expensive, or (c) rapid promotions, which are both economically and politically expensive. Corporate capitalism’s objective is to draw a profit from whatever opportunities are in reach, and often this means taking the middle territory at the expense of the excellent minority. Yeoman capitalism might be a better home for top talent, but its scaling problems and the scarcity of the substrate (resources, funding, connections) combined with top talent’s existing rarity make that match a bit of a split-maze problem; things that require two uncorrelated scarce resources in conjunction rarely happen. Supercapitalism typically courted the top talent that was unsatisfied with the slow career progress (seniority systems) and general mediocrity of the typical corporate world, but something is changing in that. The internet, and the increasing willingness of people to be honest about what they’ve seen in their careers (see: Hacker News) has created a memory. That’s a problem for supercapitalism; the sterling reputation it must maintain in order to operate, in contrast against the short-sighted and often anti-social behavior coming out of its r-selective charter (get big fast, acquire power, dominate) means that its interests are best served by a world of short-lived and unreliable memory, allowing advertisement to rule the day. VC-funded so-called “technology” companies are, for related reasons, often thrived on account of their marketing rather than technology– if they were well-marketed, they could get talent later– but the existence of an independent memory (on the Internet) threatens that.

Supercapitalism, for its part, is not having trouble getting top talent. Smart people sign up to work at hedge funds and VC-funded startups every day. It’s only going to get somewhat less of it– and paying a lot more– as the years pass, as the most talented people increasingly favor K-strategic ways of doing business. Supercapitalism isn’t going to roll up and die– not even close– and it will continue to make places for talented people. It will just, increasingly, not be the only place for top talent to go.

What remains is more of an open question than an answer. In a world where top talent is sponsored rather than employed, and in which “the vision thing” actually matters, what will the K-capitalism that grows up look like? Who will be in charge in the new world? I have no idea, but I know that we won’t get our answers from the established players– the venture capitalists and existing corporate dominators.

Statistics, cooperation, politics, and programming.

Open: a simple dice “game”

Let’s say that you’re playing a one-player “game”, where your payout (score) is determined according to the rolls of 101 dice. One of them is black and 100 are white, and your payoff is $100 times the value on the black die, plus the sum of the values on the 100 white dice. (In RPG terms, that’s d6x100 + 100d6.) The question is: how much more “important” (I’ll define this, more rigorously, below) is the black die, relative to a single one of the white dice?

Most people would say that the black die is 100 times as important; its influence on the payoff is a $500 swing (from $100 to $600) while each of the white dice has a $5 swing ($1 to $6). That would lead us to conclude that the black die is equally important as the hundred white dice, all taken together– or that the black die has 50% of the total importance. That’s not true at all. Why not? Let’s do some simulations. Here’s the code (in Clojure).

(defn white-die []
.  (inc (rand-int 6)))

(defn black-die []
.   (* 100 (inc (rand-int 6))))


(defn play []
.   (let [bd-value (black-die)
.         wd-value (reduce + 0 (repeatedly 100 white-die))]
.      (printf "Black die: %d, White dice: %d, Payoff: %d\n"
.               bd-value wd-value (+ bd-value wd-value))))

Here are some results:

user=> (play)
Black die: 100, White dice: 345, Payoff: 445
nil ;; other returns omitted.
user=> (play)
Black die: 600, White dice: 343, Payoff: 943
user=> (play)
Black die: 400, White dice: 352, Payoff: 752
user=> (play)
Black die: 100, White dice: 338, Payoff: 438
user=> (play)
Black die: 300, White dice: 322, Payoff: 622
user=> (play)
Black die: 200, White dice: 345, Payoff: 545
user=> (play)
Black die: 500, White dice: 326, Payoff: 826
user=> (play)
Black die: 300, White dice: 362, Payoff: 662
user=> (play)
Black die: 100, White dice: 353, Payoff: 453
user=> (play)
Black die: 500, White dice: 359, Payoff: 859

The quality of the payoff has a lot more to do with the black die than the white ones. A good payoff (above $700, the mean) seems to occdur if and if only if the black die roll is good (a 4, 5, or 6) because the sum of the white dice is never far from the mean value of 350. We can formalize this intuition by noting that when independent random variables are added, the variance of their sum is the sum of their variances. The variance of a 6-sided die is 35/12 (2.9166…) and so the variance of the 100 white dice, taken together, is 3500/12 = 291.666…, resulting in a standard deviation just slightly over 17. With a hundred dice being summed together, we can assume the sum of white dice to be approximately Gaussian: 99 percent of the time, the white dice will come in between $306 and $394. Even if the white dice perform terribly (say, $300) a ’6′ on the black die is going to ensure a great payoff.

While standard deviation is a more commonly used measure of dispersion, random variables cumulate according to their variance (the square of the standard deviation). The variance of the black die is not 100, but 10,000 times, that of the white die. This means that it’s 100 times more influential over the payoff than all of the white dice taken together. It contributes just over 99 percent of the variance in the payoff.

Politics

What does this have to do with human behavior and cooperation? Well, consider voting. Some people complain about the supposed “disenfranchisement” of voters in large, non-swing states such as California (reliably blue) and Texas (reliably red) under the electoral college system. When the blocs are predictable, that can be true. However, being part of a voting bloc will, in general, magnify ones’ voting power, just as the black die in the example above dominates the payoff to the point where the white dice hardly matter.

If fifteen people agree to vote the same way, they’ve increased their voting power (variance) by 225 times that of an individual, meaning that each one becomes 15 times more powerful. Let’s go a step further. Say there are 29 people in a voting body, and that simple majority is all that’s required to pass a measure. If fifteen of those agree to hold a private vote, and then vote as a bloc based on the result of that vote, the other fourteen peoples’ votes don’t matter at all, so each bloc member becomes approximately twice as individually powerful. This can further be corrupted by creating nested blocs. Eight of those people could break off and hold their own private vote and become a bloc-within-the-bloc. Of course, secrecy is required; otherwise the out-crowd of that bloc might defect. At least in theory, nothing stops a group of five people within that eight from forming a third-level bloc, and so on. This could devolve into an almost dictatorial situation where two people determine the entire vote. It isn’t always long-term stable, of course; disenfranchised people within blocs will (over time) leave, possibly joining other blocs.

One should be able to see, by now, why something like a two-party political system is so common in government. Coalitions build, because it magnifies the individual’s statistical power (percentage of the variance) to form blocs. It seems to continue until there are two coalitions in the 45 to 50 percent range, and what limits this process is that, as a coalitions grow, they become more predictable and less nimble; once they are predictable, unaffiliated (“swing”) voters have substantially more power than they should according to the principles above; while variance potential of a bloc grows as the square of its size, highly predictable blocs have very little actual variance. In other words, the equilibrium happens when the (quadratically growing) bulk power of blocs is offset by the declining true variance inherent to their predictability, leaving the few swing players as individually powerful (being unpredictable) as they would be as members of a bloc.

Economics and work

Bloc power is a major reason why collective bargaining (unionization) is such a big deal. The Brownian motion of individually unimportant workers joining and leaving a company has a minimal effect on the business. There will be good days and bad for the company due to these small fluctuations but, on the whole, an individual’s vote (whether to work or quit) is meaningless amid the noise. The low-level worker has no real vote. Collective bargaining, on the other hand, can be powerful: a large group voting against its management (a strike) at the same time can have a real impact.

The past two hundred years have proven that, without some variety of collective action, workers (even highly skilled ones) are unlikely to get a fair deal. It doesn’t matter how smart, how capable, or even how necessary they are if their votes don’t matter. There are three approaches that have been used to solve this problem (aside from a fourth, beloved by some wealthy, which is not to solve it). The first is to form a union. As much as there is a problem of corruption within unions, I don’t think any reasonable person can review history and conclude them to have been unnecessary. The second is to form a profession, which is essentially a reputation management organization that (a) keeps the individual member’s credibility high enough to keep that person employable, so he or she can challenge management, since professions require ethical obligations that supersede managerial authority (i.e. there’s no Nuremberg Defense); while (b) occasionally leveraging its role as a reputation bank to push, as a bloc, for specific causes. The third approach is a welfare state, which does not confer bloc-like power for low-level producers (i.e. workers) but (a) gives them power as consumers and, more importantly, it (b) gives individual producers the ability to refuse adverse terms of work.

These form a spectrum of solutions, with unions being the most political (an explicit bloc forms, subverting to some extent the Brownian tug-of-war that occurs in free markets and elections) while the welfare state is apolitical (it does not tell capitalists how to run their companies) while pushing a universal sea change in the market– improved leverage for workers in all industries, liberated from a month-by-month need for work income. Professions, as it were, exist between these two extremes; they are not as explicitly political or bloc-like as unions, but their ability to prevent the professional’s credibility from falling to zero– even if fired by one comp[any’s management, he’s still a member of that profession unless disbarred for ethical reasons, and will usually find new work easily– has them functioning like a private, conditional welfare state.

I’m not going to argue, among the solutions above, that any is superior to the others, or that one of those three should be favored uniformly. In fact, they don’t even conflict; societies tend to have all three of the above in some form. They seem to serve different purposes, also spanning a spectrum from local to global, like so:

  • The union exists to guarantee, as much as it can, employment at a specific company (local) on favorable terms for good-faith workers. It often wrests from management the authority to terminate people. Its downside is that, because it is an explicitly political organization, it often invents by-laws (seniority systems being the most abhorred) that reduce performance. The extreme guarantees against adverse change that unions often provide may result in a low quality of work, eroding the union’s clout in the long run. Unions are, however, the best solution when there is a small number of potential employers (oligopsony).
  • The profession exists to provide credibility (reputation) sufficient to guarantee appropriate employment, but not at a specific employer. The profession doesn’t interfere with individual terminations or promotions, nor does it often tell employers how to behave; its goal is to provide appropriate results for all good-faith members without managing a specific employer. This is more global than the union, because professionals may have to move to different companies or geographic locations to take advantage of the profession’s auspices, but more local than a welfare state because it focuses on a specific class of workers. Professions work well when there is a large and changing set of potential employers, but over a fairly fixed scope of work.
  • The welfare state (a global solution, as it involves a definition of social justice that a central government attempts to enforce uniformly) doesn’t guarantee market employment at all. It does, however, attempt to create an economic floor below which people cannot fall. Even if they lose power as producers (because the market may not want anything they can make) they retain some power as consumers. The moral purpose of this is two-fold. First, unneeded workers can retrain and become viable producers. Second, the welfare state’s existence gives workers enough leverage that they stand a chance at getting a fair deal– without necessarily having to form collectives in order to do it. Welfare states do the best job at the large-scale, society-wide problems; for example, they can provide education and training for those who have not yet entered a union or profession.

What’s most relevant to all this, however, is that collective action is as relevant today as it was 100 years ago. There are a lot of people who claim, for example, that labor unions “were good in their time, but have served their purpose”. I don’t think that’s true. There are, of course, many problems with existing labor unions and with the professions, but the statistical politics underlying their formation is still quite relevant.

Technology

Software engineers in particular are a group of people who’ve never fully decided whether they want to be blue-collar (making unionization a relevant strategy) or white-collar (necessitating a profession). It’s not clear to me that either of these approaches, as commonly imagined, will do what we need in order to get programmers fairly paid and their work reasonably evaluated. I would argue, however, that the existing culture of free agency seems to be leading nowhere. Software and hardware engineers, in addition to designers and operational people, need to develop a common tribal identity as makers. Otherwise, management will continue to run divide-and-conquer strategies against them that leave them with the worst of both the blue-collar and white-collar worlds: the low autonomy and job security of an hourly wage worker, but the unreasonable expectations and long hours associated with salarymen.

The needs of the most creative and effective technology workers should be given consideration; maker culture is becoming a real thing, and the societies and organizations that prosper in the next fifty years will be those that find a way to contend with it. Thanks to personal computing, the internet, and quite likely 3D printing, we’re coming into an era in which the zero-sum approach to resources that has existed for thousands of years no longer makes sense. Copying a book used to be a painstaking, miserable process. (The reason for the beautiful calligraphy and illustrations in hand-copied medieval books is that the work would be intolerable without some room for creative flourish.) Now it’s a Unix command that takes less than second. Information scarcity is rapidly ending and of more interest is the culture (maker culture) that has sprung up around that, starting in the open source world that is making its way into software, which is structurally cooperative.

Maker culture is centered on the positive-sum worldview that makes sense in such a world. Makers tend to no longer see each other as competitors amid existing scarcity; rather, the greater war is against scarcity itself.

Good programmers no longer buy in to traditional industrial competition. They’d rather work on open source projects that improve the world (and their own individual reputations) than line the corporate war chest, because the benefits of tapping into the larger society (open source economy) are much greater, not only for them but often also for their employers, than those of restricting themselves to one corporate silo.  They’ll work on closed-source “secret sauce” projects in a somewhat privileged (“ninja”) position, but not in the commoditized role associated with the “code monkey” appellation. Those jobs, as portrayed less than affectionately in the movie Office Space, are going to die out.

In twenty years, top maker talent will no longer be employed so much as it is sponsored. This will be good for the world, as it will generate a much more cooperative economy than what existed before it, but a large number of organizations will find themselves unable to adapt and will fail.

Gervais / MacLeod 24: Fundamental Theorem of Employment

In analyzing the economics and sociology of office-style Work, an inefficient set of institutional patterns that affects hundreds of millions of people, I’ve often had to ask the question, “Why are so many jobs so bad?” Plenty of positions are inaccurately or dishonestly advertised, many shouldn’t exist at all, and job openings that should exist often don’t. What’s going on with all this? And how should an individual person choose jobs, in light of the inefficient market? I’ve come to a conclusion that, despite the complexity of these issues, is refreshingly simple and, while failing to capture all cases, surprisingly powerful and appropriate to the vast majority of jobs. I might call it the Fundamental Theorem of Employment (FTOE).

A person is hired to do work that the hiring person (a) cannot do for himself, or (b) does not want to do.

Corollary: It is extremely important to know which of the two is the case.

These are, in general, two different cases. A person hires a maid to do undesirable work of which most people are capable, while he hires a doctor to do work that he can’t do for himself. It’s essential for each person to know which of the two cases applies to his or her job. Most jobs can be clearly delineated as one or the other. We’ll call the first category of jobs– a person is hired to bring expertise, skill, or capacity that the hiring manager does not have– “Type 1″; and the “boss doesn’t want to do” jobs, “Type 2″.

In a Type 1 job, you have leverage and you get respect because you’re delivering labor that the manager (a) does not have the ability to render himself, and (b) much more importantly, cannot accurately evaluate. Your boss is forced to trust you. Often, he will trust you just to reduce his own cognitive dissonance. In a Type 2 job, you rarely get any respect; you’re just there to do the worst of the work. You’re not trusted very far, and your manager thinks he can do your job just as well and twice as fast. In the career game, getting stuck in the Type 2 world is a losing proposition.

That seems simple enough, and the advice derived from it is fairly traditional. Build skills. Develop expertise. Become a “unicorn” (a person whose combination of skills makes her unusually rare and confers leverage). Get Type 1 jobs. The real world, of course, isn’t quite so simple; and it might be hard for an individual to tell which of the two possibilities applies to her job. I’m here to tackle some of the more complex cases that pop up in reality, and analyze which dynamic of behavior is more accurate to each.

Below are some cases that don’t necessarily fall into a clear Type 1 vs. Type 2 delineation, and require further analysis.

Excess capacity. Most large companies don’t hire for a specific role, so much as they increase (or decrease) their total headcount based on business needs, cash flow, and economic projections. Companies”don’t hire specifically for Type 1 or Type 2 work; they’re concerned with the economics, not sociology. Most people, in truth, are hired into firms to serve as “excess capacity”; that is, hired into a general-purpose labor pool so there is some slack in the schedule and there are internal candidates for vacancies. Whether a person ends up in Type 1 or Type 2 work isn’t driven by some abstract “general will” of the firm but by the needs of specific managers where that person lands. Unfortunately, this often puts a person into Type 2 work by default.

Depending on the company, the manager of the new employee’s team might not have had any input into the hiring of that person. Sometimes, the company just says, “here are some guys”, and that tends to result in a lot of undesirable work being offloaded onto them. Or, that person may have been hired for a position that was shortly after filled internally, or made redundant, leading to a need to make work for the new hire. The point of all this is that if you can’t identify (and preferably quickly) some X for which (a) a manager needs X, (b) the managers knows he needs X, and (c) you’re very good at X; you just become a fresh hire looking for something to do.

Simply being “excess capacity” isn’t necessarily bad. If there’s honestly about the fact, then management can set an appropriate arrangement. “You can work on whatever you want most of the time, but when you’re needed, you’re expected to be available.” Then, a person has the time and allowance to seek Type 1 work where he or she will add more value. Some companies explicitly set aside time for self-directed work (e.g. 20% time) in acknowledgment of the need for slack in the schedule. Others do not, and fall into a Type-2-driven default pattern of rippling delegation.

In large companies, people are hired for macroeconomic reasons that don’t conform to the Type 1 vs. 2 delineation explicitly, leaving the question unanswered: does the employee become a respected advisor whose expertise confers a certain automatic credibility, or a grunt to which the worst work is delegated?

Automation

Especially relevant to technical work is the role of automation. If work is undesirable, someone will try to “kill” it by programming a computer to do it faster and more reliably than a human. For many business processes, this is easy. For some, it’s quite hard. For example, it took years of research into machine learning before computers could accurately read hand-written addresses. At any rate, computers turn out to be perfect repositories for the worst of the Type 2 work that no one wants to do. They do it without complaint, and much faster. They’re cheap, as well. This is winning for everyone.

Computer programming has its own weird interaction with the FTOE. Business problems were traditionally solved with lots of low-paid and ill-respected manpower, so corporate growth mostly came down to the delegation of Type-2 labor as the beast grew. However, the magic of software engineering is that a small bit of more challenging, more fun work (automating painful processes so that the task is complete forever before the novelty of the new job wears off) can replace a larger amount of bland, tedious work. Most of business growth is about Type-2 hiring: bringing in more people to do the work that the bosses don’t want to do. A competent software engineer can take on the Type-1 task of automating all that junk work– if management trusts her to do so.

Management doesn’t, in general, care how the mountain of traditionally undesirable work is done. If it’s done well by ten bored humans who occasionally quit or fail but are easy enough to replace, that’s the familiar “devil you know”. If someone else can come along and perform the much more enjoyable task of automating that work for good, that’s better because it saves a lot of money and pain. Sort-of. There’s a problem here, and it’s one that every software engineer and software manager must understand.

The relationship between software engineers and management is fraught with conflict. There are few industries where there is more tribal dislike between workers and management than in software, and the problem isn’t the people so much as the interaction of incentives and risks. Software itself (like any industry) generates a lot of undesirable (Type 2) work; but in software, there’s almost always a way of automating the bland work away– a hard, Type 1, sort of job. The danger of that is that the automation of undesirable work might take more time than simply completing it, while the engineer’s impulse (which is almost irresistible) is automate immediately and without regard to cost.

This provides two very different paths to completion: one that is low in variability but boring, the other being more fruitful but riskier. What goes wrong? Without diverging into another subtopic, management participates more fully in an employee’s downside than upside risks– if the engineer does great work, it reflects on that engineer; but if the engineer fails expensively, it reflects on the management– so managers tend to favor low-risk strategies for that reason alone. It’s not that software engineers or managers or bad people; the risks are just improperly aligned.

Solving this problem– aligning incentives and structuring companies to take advantage of opportunities for automation, which almost always improve the firm’s success in the long term– would require another essay.

Defensive rejection

Above, I’ve proposed that people hire others to do work in one of two cases: undesirable work, and work that the person doing the hiring can’t perform. There isn’t always such a clean-cut distinction. Most people don’t have the humility to recognize their limitations, and so they tend to overestimate their ability to perform work that they know little about. The extreme case of this is defensive rejection, in which a person denigrates a class of work as being menial, unimportant, or trivial to compensate for a lack of knowledge about it.

Many software engineers are going to recognize that the attitude of “the business” toward their work is often a case of defensive rejection, and that’s right. But we, as a group, are far from innocent on that front. We tend to take the same attitude toward marketing and business people. The truth is that the good ones are highly capable in ways that most of us are not; most of us just lack the basic competence to separate the good ones from the bad. When one lacks visibility into a field of work, one tends to associate all people who do it with the average competence of the group, which usually leads to an unflattering stereotype for any high position (because most people in it are, in fact, unqualified to hold it). That leads to the incorrect conclusion (also seen with politicians, of whom the average performance is poor) that “none of them are any good”. 

When defensive rejection is in play, the underlying truth is that the manager is hiring in type 1; the employee is brought on to do work that the manager can’t do for himself. Unfortunately, the manager’s insecurity and hubris generate a type-2 context of “I could do that stuff if I wanted to”. The subtext becomes that the work is bland, detail-oriented dreck that the manager is too important to learn. This is the most frustrating type of job to be in; one where the boss thinks he can do your job but actually can’t. It means you have to deal with unreasonable expectations despite low overall status and perceived value to him and to the company as a whole. That’s horrible, but it’s also freakishly common as far as scenarios go, and it leads to the engineer feeling set up to fail– asked to do impossible things, then treated poorly when inevitable failure occurs.

Apprentice systems

There’s one other scenario that doesn’t fit nicely into the Type 1 vs. 2 delineation: the apprentice (or protege) context. At first thought, apprenticeship might seem to be strictly Type 2, since most of the work that apprentices spend their time on is make-work that has ceased being interesting to superior craftsmen. However, apprentices bring a Type-1 function by being able to do one thing the master cannot: perpetuate the work (and, more importantly, the upkeep of a valued tradition or institution) through time. If you’re sixty years old, a twenty-year-old apprentice can continue the work forty years (on average) longer than you can.

Modern private-sector corporations don’t have much use for apprentice structures and guild cultures, because they no longer see that far into the future. No CEO gets job security by setting up a culture of mentorship that might yield excellence ten years down the road. In this next-quarter culture, apprentice systems have mostly been thrown overboard. Long-term vision is far out of style for most modern corporations.

That said, there’s a value in understanding this old-style system. Why? Because even managers are uncomfortable with the naked parasitism of Type-2 employment (e.g. “I’m just hiring you to do the crap I don’t want to do, while I fill my time with the career-building and fun work”) and often attempt to recast the role as an apprenticeship opportunity. That is, at least, how every subordinate job is presented; an opportunity to learn the skills necessary to get to the next step. There are varying degrees of earnestness in this– some managers truly see their reports as proteges, while others see them as mere subordinates.

In negotiation theory, this is sometimes called a standard: a promise that is understood not to be fully delivered (most people realize that most bosses just see their reports as repositories for undesirable work, and that the apprentice metaphor is mostly rhetorical) but that may still be cited in policy to get an arrangement more in accord with that standard than one might otherwise get (“appealing to the standard”). Even people in power are uncomfortable explicitly departing (“breaking the standard”) from something previously promised.

If you want to move from Type-2 to Type-1 employment (and, believe me, you should) then the first thing you have to do is get qualified for that kind of work; the best way to make sure your boss gives you appropriate work (to gain that qualification and validation) is to continually appeal to the standard of the master/apprentice relationship– and hope that your manager doesn’t have the audacity to break the standard.

Why is FTOE important?

It’s important to understand the Fundamental Theorem (and being trained as a mathematician, I know it’s not actually a theorem so much as an observation) of Employment, above, because people tend to discuss conceptions of “the job market” as if they were forces of nature. They’re not. A job exists because someone needs or wants another person to perform work, and the expensiveness of that generally means that one of two cases applies: the person doesn’t want to do that work, or the person cannot do that work. Regardless of the work itself, the social contexts that arise from those two subcategories could not be more different. It’s very important to know which one applies.

The advice that comes out of this is to find a way to qualify oneself for the Type 1 work. That’s harder than it looks. Becoming good at highly-skilled work is the first half of the battle, but there’s a social component that can’t be ignored. Software engineering is a prime example of that. The whole point of the bastardization of “object oriented programming” (which, by the way, has become the exact opposite of Alan Kay’s vision of it) that has grown up in the enterprise is to coerce software engineering into Type 2 commodity work. Having generating scads of low-quality, brittle code, it can be called a failure. Yet that mentality persists in the world of corporate software engineering, and it will be a while before the business starts to recognize software as Type 1 work.

While one is progressing through the validation process that is more drawn-out than building the skill set, I think there are two key strategic necessities. The first, again, is to appeal to the standard (as above) and re-cast any Type 2 social context in employment as a mentor/protege role. The second, and more importantly, is to always drive toward a Type 1 context. The question should be asked: “What am I here to deliver that no one else can?”

The Disentitled Generation

Anyone else up for some real rage? I can’t promise that there won’t be profanity in this post. In fact, I promise that there will be, and that it will be awesome. Let’s go.

People don’t usually talk about these things that I talk about, for fear that The Man will tear their fucking faces off if they tell the truth about previous companies and how corporate office really run themselves, but I am fucking sick of living in fear. One can tell that I have an insubordinate streak. It’s a shame, because I am extremely good at every other fucking thing the workplace cares about except subordination; but that’s one thing I never got down, and while it’s more important (in the office context) than any other social skill, I’m too old to learn it.

Let’s talk about the reputation that my generation, the Millennials (born ca. 1982 to 2000), has for being “entitled”. This is a fun topic.

I’ve written about why so-called “job hopping” doesn’t deserve to be stigmatized. Don’t get me wrong: if someone leaves a generally good job after 9 months only because he seeks a change of scenery, then he’s a fucking idiot. If you have a good thing going, you shouldn’t seek a slightly better thing every year. Eventually, that will blow up in your face and ruin your life. Good jobs are actually kinda rare. I repeat: if you find a job that continues to enhance your career and that doesn’t make you unhappy, and you don’t stick with it for a few years, then you’re an idiot. You should stay when you find something good. A genuine mentor is rare and hard to replace. That’s not what I’m talking about here.

The problem? Most jobs aren’t good, or don’t make sense for the long term. Sometimes, the job shouldn’t exist in the first place, provides no business value, and is terminated by one side or the other, possibly amicably. Sometimes, the boss is a pathological micromanager who prevents his reports from getting anything done, or an extortionist thug who expects 100% dedication to his career goals and gives nothing in return. Sometimes, people are hired under dishonest pretenses. Hell, I’ve seen startups hire three people at the same time for the same leadership position, without each other’s knowledge of course. Sometimes, management changes that occur shortly after a job is taken turn a good job into an awful one. This nonsense sounds very uncommon, right? No. Each of these pathologies is individually uncommon, but there are so many failure modes for an employment relationship that, taken in sum, they are common. All told, I’d say that about 40 percent of jobs manage to make it worthwhile to keep showing up after 12 months. Sometimes, the job ends. It might be a layoff for business reasons. Sometimes it’s a firing that may not even be the person’s fault. Most often, it’s just pigeonholing into low-importance, career-incoherent work, leaving the person to get the hint that she wasn’t picked for better things and leave voluntarily. Mostly, this political injection is random noise with no correlation to personal quality. Still, I think it’s reasonable to say that 60% of new jobs fail in the first 12 months (even if many go into a “walking dead” state where termination is not a serious risk, but in which it’s still pointless and counterproductive to linger). That means 13 percent of people are going to draw four duds for reasons that are no fault of their own. One in eight people, should they do the honest and mutually beneficial thing which is to leave a job when it becomes pointless, becomes an unemployable job hopper. Seriously, what the fuck?

So let me get one thing out there. Not only is the “job hopping” stigma outdated, it’s wrong and it’s stupid. If you still buy into the “never hire job hoppers” mentality, you should fucking stop using your company as a nursing home and instead, for the good of society, use an actual nursing home as your nursing home. I’m serious. If you really think that a person who’s had a few short-term jobs deserves to be blacklisted over it when the real corporate criminals thrive, then letting you make decisions that affect peoples’ lives is like letting five-year-olds fly helicopters, and you should get the fuck out of everything important before you do any more damage to peoples’ lives and the economy. I’m sorry, but if you cling to those old prejudices, then the future has no place for you.

It needed to be said. So I did.

The “job hopping” stigma is one rage point of mine, but let’s move to another: our reputation as an “entitled” Millennial generation. Really? Here are some of the reasons why we’re considered entitled by out-of-touch managers:

  1. We “job hop” often, tending to have 4 to 6 jobs (on average) by age 30.
  2. We expect to be treated as colleagues and proteges rather than subordinates.
  3. After our first jobs, we lose interest in “prestigious” institutions, instead taking a mercenary approach that might favor a new company, or no company. 
  4. We push for non-conventional work arrangements, such as remote work and flex-time. If we put in 8 hours of face time, we expect direct interest in our careers by management because (unlike prior generations who had no choice) we consider an eight-hour block a real sacrifice.
  5. We question authority.
  6. We expect positive feedback and treat the lack of it as a negative signal (“trophy kids”).

Does this sound entitled? I’ll grant that there’s some serious second-strike disloyalty that goes on, with a degree of severe honesty (what is “job hopping” but an honesty about the worthlessness of most work relationships?) that would have been scandalous 30 years ago, but is it entitled? That word has a certain meaning, and the answer is “no”.

To be entitled, as a pejorative rather than a matter-of-fact declaration about an actual contractual agreement, implies one of two things:

  1. to assume a social contract where none exists (i.e. to perceive entitlement falsely.)
  2. to expect another party to uphold one side of an existing (genuine) social contract while failing to perform one’s own (i.e. one-sided entitlement).

Type I entitlement is expressed in unreasonable expectations of other people. One example is the “Nice Guy Syndrome“, wherein a man expects sexual access in return for what most people consider to be common courtesy. The “Nice Guy” is assuming a social contract between him and “women” that neither exists nor makes sense. Type II is the “culture of entitlement” sometimes associated with a failed welfare state, wherein generationally jobless people– who, because they have ceased looking for work, are judged to be failing their end of the social contract– continue to expect social services. These are people whose claims are rooted in a genuine social contract– the welfare state’s willingness to provide insurance for those who continually try to make themselves productive, but fail for reasons not their fault– but don’t hold up their end of the deal.

So, do either of these apply to Millennials? Let me assess each of the six charges above.

1. Millennials are “job hoppers”. There’s some truth in that one. The most talented people under 30 are not going to stick around in a job that hurts their careers. We’re happy to take orders and do the less interesting work for a little while, if management assists us in our careers, with an explicit intent to prepare us for more interesting stuff later. Failing that, we treat the job as a simple economic transaction. We’re not going to suffer a dues-paying evaluative period for four years when another company’s offering a faster track. Or, if we’re lucky, we can start our own companies and skip over the just-a-test work entirely and do things that actually matter right away. Most of us have been fired or laid off “at will” at least once, and we have no problem with this new feature (job volatility) of the economy. None of us consider lifelong employment an entitlement or right. We don’t expect long-term loyalty, nor do we give it away lightly.

2. Millennials “expect” to be treated as proteges. Not quite. Being a cosmopolitan, well-studied generation exposed to a massive array of different concepts and behaviors from all over the world, we expect very little of other people. We’ve seen so much that we realize it’s not rational to approach people with any major assumptions. The world is just too damn big and complicated to believe in global social contracts. Getting screwed doesn’t shock or disgust or hurt us. It doesn’t thwart our expectations, because we don’t really have any. We simply leave, and quickly. For us, long-term loyalty is the exception, and yes, we’re only going to stay at a job for 5 years if it continues to be challenging and beneficial to our careers. That’s not because we “expect” certain things, and we aren’t “making a statement” when we change jobs. It’s not personal or an affront or intentional “desertion”. We can do better, that’s all.

3. Millennials don’t have respect for prestige and tradition. Yes and no. We don’t start out that way. The late-2000s saw one of the most competitive college admissions environments in history. Then there’s the race to get into top graduate departments or VC-darling startups or investment banking– the last of these being the Ivy League of the corporate world. Then something happens. Around 27, people realize that that shit doesn’t matter. You can’t eat prestige, and many of the most prestigious companies are horrible places to work. Oh, and we think we’re hot shit until we get our asses handed to us by superior programmers and traders from no-name universities and learn that their educations were quite good as well. We realize that work ethic and creativity and long-term diligence and deliberate practice are the real stuff and we lose interest in slaving away for 90 hours per week just because a company has a goddamn name.

4. Many of us expect non-conventional work/life arrangements. This is true, and there’s a reason for it. What is the social contract of an exempt salaried position, under which hourage expectations are only defined by social expectations rather than contract? As far as I can tell, there are two common models. Model A: worker produces enough work not to get fired, manager signs a check. Model B: worker puts a serious investment of self and emotional energy into the work as a genuine working relationship would involve, and management returns the favor with career support and coherence. Under either model, the 8-hour workday is obsolete. Model A tells us that, if a worker can put in a 2-hour day and stay employed, he’s holding up his end of the deal, and it’s management’s fault for not giving him interesting work that would motivate him to perform beyond the minimum. Model B expects a mutual contract of loyalty to each other’s interests, but does not specify a duration or mode of work. Model B might be held to generally support in-office work with traditional hours, for the sake of collaboration and mentoring, but that opens up a separate discussion, especially in the context of individual differences regarding when and how people work best.

5. Millennials question authority. True, and that’s a virtue. Opposing authority because it is authority is no better than being blindly (or cravenly) loyal to it, but questioning it is essential. People who are so insecure that they can’t stand to be questioned should never be put in leadership positions; they don’t have the cojones for it. I question my own ideas all the time; if you expect me to follow you, then I will question yours. It’s a sign of respect to question someone’s ideas, not a personal challenge. It’s when smart people don’t question your ideas that you should be worried; it means they’ve already decided you’re an idiot and they will ignore or undermine you. 

6. We expect positive feedback and respond negatively to a lack of acknowledgement. That’s true, but not because we believe “everyone’s a winner”. If anything, it’s the opposite. We know that most people lose at work and would prefer to play a different game when that appears likely to happen. No, it’s not about “trophies”. A trophy is a piece of plastic. We get bored unless there’s a real, hard-to-fake signal that we aren’t wasting our time. Not a plastic trophy, but management that takes our career needs seriously and complete autonomy over our direction. We know that most people, in their work lives, end up with incompetent or parasitic bosses who waste years of their time on career-incoherent wild goose chases, and we refuse to be on the butt of that joke. Does this mean that we’re not content to be “average”, and that we require being on the upside of a zero-sum executive favoritism to stay engaged with our work? Well, in order to have it not be that way, you need to create a currently-atypical work environment where average people don’t end up as total losers. With all the job hopping we do, we don’t care about relative measures of best or better. We want good. Make a job good and people won’t worry about what others around them are getting.

I think, with this exposition, that there’s a clear picture of the Millennial attitude. Yes, we take second-strike disloyalty to a degree that, even ten years ago, would be considered insolent, brazen, and even reckless in the face of the career damage done (even now) to the job-hoppers. We’ve grown bolder, post-2008. Quit us, and we quit. It’s not that we like changing jobs every few months– believe me, we fucking don’t. We’re looking for the symbiotic 5- or 10-year-fit, as any rational person would, but we’re not going to lie to ourselves for years– conveniently paying dues on evaluative nonsense work while our bosses spend half-decades pretending to look for a real use for our underutilized talents (only to throw us out in favor of fresher, more clueless, younger versions of ourselves)– after drawing a dud.

Is the Millennial attitude exasperating for older managers, used to a higher tolerance for slack on matters of career coherency? I’m sure it is. I’m sure that the added responsibility imposed by a generation characterized by fast flight is unpleasant. It is not, however, entitled. It’s not Type I entitlement because we don’t assume the existence of a social contract that was never made. We only hold employers to what they actually promise us. If they entice us with promises of career development and interesting work, then we expect that. If they’re honest about the job’s shortcomings, we respect that, too. But we only expect the social contract that we’re explicitly given. I’d also argue that it’s not Type II entitlement because Millennials are, when given proper motivation, very hard-working and creative. We want to work. We want genuine work, not bullshit meetings to make the holder of some sinecure feel important.

What are we, if not “entitled”? We’re the opposite. We’re a disentitled generation. We never believed in the corporate paternalist social contract, and most of us are comfortable with this brave new world that has followed its demise. Yes, we’re mercenary. We respond in kind (in fact, often disproportionately) to genuine loyalty, but we’re far too damn honest to pretend we’re getting a good deal when we’re thrown into a three-year dues-paying period rendered obsolete in a world where fast advancement is possible and fast firing is probable for those who don’t advance. I’m in software, where, by age 35, becoming a technical expert (you need a national reputation in your specialty if you want to be employable as a programmer on decent terms by that age) or an executive becomes mandatory. As this leaves 13 years to “make a mark”, one simply will not find people willing to endure a years-long dues-paying period that one would want to hire. Asking someone to risk 2 of those 13 years on dues-paying (that might lead nowhere) is like asking a person to throw 15 percent of her net worth into a downside-heavy investment strategy with no potential for diversification– a bad idea. Reasonable dues-paying arrangements may have existed under the old corporate social contract of cradle-to-grave institutional employment, but that’s extinct now. So should be the “job hopper” stigma and the early-stage dementia patients who still believe in it.

Gervais / MacLeod 21: Why Does Work Suck?

This is a penultimate “breather” post, insofar as it doesn’t present much new material, but summarizes much of what’s in the previous 20 essays. It’s now time to tie everything together and Solve It. This series has reached enough bulk that such an endeavor requires two posts: one to tie it all together (Part 21) and one to discuss solutions (Part 22). Let me try to put the highlights from everything I’ve covered into a coherent whole. That may prove hard to do; I might not succeed. But I will try.

This will be long and repeat a lot of previous material. There are two reasons for that. First, I intend this essay to be a summarization of some highlights from where we’ve been. Second, I want it to stand alone as a “survey course” of the previous 20 essays, so that people can understand the highlights (and, thus, understand what I propose in the conclusion) even if they haven’t read all the prior material.

If I were to restart this series of posts (for which I did not intend it, originally, to reach 22 essays and 92+ kilowords) I would rename it Why Does Work Suck? In fact, if I turn this stuff into a book, that’s probably what I’ll name it. I never allowed myself to answer, “because it’s work, duh.” We’re biologically programmed to enjoy working. In fact, most of the things people do in their free time (growing produce, unpaid writing, open-source programming) involve more actual work than their paid jobs. Work is a human need.

How Does Work Suck?

There are a few problems with Work that make it almost unbearable, driving it into such a negative state that people only do it for the lack of other options.

  • Work Sucks because it is inefficient. This is what makes investors and bosses angry. Getting returns on capital either requires managing it, which is time-consuming, or hiring a manager, which means one has to put a lot of trust in this person. Work is also inefficient for average employees (MacLeod Losers) which is why wages age so low.
  • Work Sucks because bad people end up in charge. Whether most of them are legitimately morally bad is open to debate, but they’re certainly a ruthless and improperly balanced set of people (MacLeod Sociopath) who can be trusted to enforce corporate statism. Over time, this produces a leadership caste that is great at maintaining power internally but incapable of driving the company to external success.
  • Work Sucks because of a lack of trust. That’s true on all sides. People are spending 8+ hours per day on high-stakes social gambling while surrounded by people they distrust, and who distrust them back.
  • Work Sucks because so much of what’s to be done in unrewarding and pointless. People are glad to do work that’s interesting to them or advances their knowledge, or work that’s essential to the business because of career benefits, but there’s a lot of Fourth Quadrant work for which neither applies. This nonsensical junk work is generated by strategically blind (MacLeod Clueless) middle managers and executed by rationally disengaged peons (MacLeod Losers) who find it easier to subordinate than to question the obviously bad planning and direction.

All of these, in truth, are the same problem. The lack of trust creates the inefficiencies that require moral flexibility (convex deception) for a person to overcome. In a trust-sparse environment, the people who gain people are the least deserving of trust: the most successful liars. It’s also the lack of trust that generates the unrewarding work. Employees are subjected, in most companies, to a years-long dues-paying period which is mostly evaluative– to see how each handles unpleasant make-work and pick out the “team players”. The “job” exists to give the employer an out-of-the-money call option on legitimately important work, should it need some done. It’s a devastatingly bad system, so why does it hold up? Because, for two hundred years, it actually worked quite well. Explaining that requires delving into mathematics, so here we go.

Love the Logistic

The most important concept here is the S-shaped logistic function, which looks like this (courtesy of Wolfram Alpha):

The general form of such a function L(x; A, B, C) is:

where A represents the upper asymptote (“maximum potential”), B represents the rapidity of the change, and C is a horizontal offset (“difficulty”) representing the x-coordinate of the inflection point. The graph above is for L(x; 1, 1, 0).

Logistic functions are how economists generally model input-output relationships, such as the relationship between wages and productivity. They’re surprisingly useful because they can capture a wide variety of mathematical phenomena, such as:

  • Linear relationships; as B -> 0, the relationship becomes locally linear around the inflection point, (C, A/2).
  • Discrete 0/1 relationships: as B -> infinity, the function approaches a “step function” whose value is A for x > C and 0 for x < C.
  • Exponential (accelerating) growth: If B > 0, L(x; A, B, C) is very close to being exponential at the far left (x << C). (Convexity.)
  • Saturation: If B > 0, L(x; A, B, C) is approaching A with exponential decay at the far right (x >> C). (Concavity.)

Let’s keep inputs abstract but assume that we’re interested in some combination of skill, talent, effort, morale and knowledge called x with mean 0 and “typical values” between -1.0 and 1.0, meaning that we’re not especially interested in x = 10 because we don’t know how to get there. If C is large (e.g. C = 6) then we have an exponential function for all the values we care about: convexity over the entire window. Likewise, leftward C values (e.g. C = -6) give us concavity over the whole window.

Industrial work, over the past 200 years, has tended toward commoditization, meaning that (a) a yes/no quality standard exists, increasing B, and (b) it’s relatively easy for most properly set-up producers to meet it most of the time (with occasional error). The result is a curve that looks like this one, L(x; 10, 4.5, -0.7), which I’ll call a(x):

Variation, here, is mainly in incompetence. Another way to look at it is in terms of error rate. The excellent workers make almost no errors, the average ones achieve 95.8% of what is possible (or a 4.2% error rate) with the mediocre (x = -0.5) making almost 5 times as many mistakes (28.9% error rate), and the abysmal unemployable with an error rate well over 50%. This is what employment has looked like for the past two hundred years. Why? Because an industrial process is better modeled as a complex network of these functions, with outputs from one being inputs to another. The relationship of individual wage into morale, morale into performance, performance into productivity, and individual productivity into firm productivity, and firm productivity into profitability, can all be modeled as S-shaped curves. With this convoluted network of “hidden nodes” that exists in a context of a sophisticated industrial operation, it’s generally held to be better to have a consistently high-performing (B high, C negative) node than higher-performing but variable node.

One way to understand the B in the above equation is that it represents how reliably the same result is achieved, noting the convergence to a step function as B goes to infinity. In this light, we can understand mechanization. Middle grades of work rarely exist with machines. In the ideal, they either execute perfectly, or fail perfectly (and visibly, so one can repair them). Further refinements to this process are seen in the changeover from purely mechanical systems to electronic ones. It’s not always this way, even with software. There are nondeterministic computer behaviors that can produce intermittent bugs, but they’re rare and far from the ideal.

As I’ve discussed, if we can define perfect performance (i.e. we know what A, the error-free yield, looks like) then we can program a machine to achieve it. Concave work is being handed over to machines, with the convex tasks remaining available. With convexity, it’s rare that one knows what A and B are. On explored values, the graph just looks like this one, for L(x; 200, 2.0, 1.5), which I’ll call b(x):

It shows no signs of leveling off and, for all intents and purposes, it’s exponential. This is usually observed for creative work where a few major players (the “stars”) get outsized rewards in comparison to the average people.

Convexity Isn’t Fair

Let’s say that you have two employees, one of whom (Alice) is slightly above average (x = 0.1) and the other of whom (Bob) is just average (x = 0.0). You have the resources to provide 1.0 full point of training, and you can split it anyway you choose (e.g. 0.35 points for Alice, and 0.65 points for Bob). Now, let’s say that you’re managing concave work modeled by the function L(x; 100, 2.0, -0.3), which is concave.

Let the x-axis represent the amount of training (0.0 to 1.0) given to Alice, with the remainder given to Bob. Here’s a graph of their individual productivity levels, with Alice in blue, Bob in purple, and their sum productivity in the green curve

If we zoom in to look at the sum curve, we see a maximum at x = 0.45, an interior solution where both get some training.

At x = 0.0 (full investment in Bob) Alice is producing 69.0 points and Bob’s producing 93.1, for a total of 162.1.

At x = 0.5 (even split of training) Alice in producing 85.8 points and Bob’s producing 83.2, for a total of 169.0.

At x = 1.0 (full investment in Alice) Alice is producing 94.3 points and Bob’s producing 64.6, for a total of 158.9.

The maximal point is x = 0.45, which means that Alice gets slightly less training because Bob is further behind and needs it more. Both end up producing 84.55 points, for a total of 169.1. After the training is disbursed, they’re at the same level of competence (0.55). This is a “share the wealth” interior optimum that justifies sharing the training.

Let’s change to a convex world, with the function L(x; 320, 2.0, 1.1). Then, for the same problem, we get this graph (blue representing Alice’s productivity, purple representing Bob’s, and the green curve representing the sum):

Zooming in on the graph sum productivity, we find that the “fair” solution (x = 0.45) is the worst!

At x = 0.0 (full investment in Bob) Alice is producing 38.1 points and Bob’s producing 144.1, for a total of 182.2.

At x = 0.5 (even split of training) Alice in producing 86.1 points and Bob’s producing 74.1, for a total of 160.2.

At x = 1.0 (full investment in Alice) Alice is producing 160.0 points and Bob’s producing 31.9, for a total of 191.9.

The maxima are at the edges. The best strategy is to give Alice all of the training, but giving all to Bob is better than splitting it evenly, which is about the worst of the options. This is a “starve the poor” optimum. It favors picking a winner and putting all the investment into one party. This is how celebrity economies work. Slight differences in ability lead to massive differences in investment and, ultimately, create a permanent class of winners. Here, choosing a winner is often more important than getting “the right one” with the most potential.

Convexity pertains to decisions that don’t admit interior maxima, or for which such solutions don’t exist or make sense. For example, choosing a business model for a new company is convex, because putting resources into multiple models would result in mediocre performance in all of them, thus failure. The rarity of “co-CEOs” seems to indicate that choosing a leader is also a convex matter.

Convexity is hard to manage

In optimization, convex problems tend to be the easier ones, so the nomenclature here might be strange. In fact, this variety of convexity is the exact opposite of convexity in labor. Optimization problems are usually framed in terms of minimization of some undesirable quantity like cost, financial risk, statistical error, or defect rate. Zero is the (usually unattainable) perfect state. In business, that would correspond to the assumption that an industrial apparatus has an idealized business model and process, with the management’s goal to drive execution error to zero.

What makes convex minimization methods easier is that, even in a high-dimensional landscape, one can converge to the optimal point (global minimum) by starting from anywhere and iteratively stepping in the direction recommended by local features (usually, first and second derivative). It’s like finding the bottom point in a bowl. Non-convex optimizations are a lot harder because (a) there can be multiple local optima, which means that starting points matter, and (b) the local optima might be at the edges, which has its own undesirable properties (including, with people, unfairness). The amount of work required to find the best solutions is exponential in the number of dimensions. That’s why, for example, computers can’t algorithmically find the best business model for a “startup generator”. Even if it were a well-formed problem, the dimensionality would be high and the search problem intractable (probably).

Convex labor is analogous to non-convex optimization problems while management of concave labor is analogous to convex optimization. Sorry if this is confusing. There’s an important semantic difference to highlight here, though. With concave labor, there is some definition of perfect completion so that error (departure from that) can be defined and minimized with a known lower bound: 0. With convex labor, no one knows what the maximum value is, because the territory is unexplored and the “leveling off” of the logistic curve hasn’t been found yet. It’s natural, then, to frame that as a maximization problem without a known bound. With convex labor, you don’t know what the “zero-or-max” point is because no one knows how well one can perform.

Concave labor is the easy, nice case from a managerial perspective. While management doesn’t literally implement gradient descent, it tends to be able to self-correct when individual labor is concave (i.e. the optimization problem is convex). If Alice starts to pull ahead while Bob struggles, management will offer more training to Bob.

However, in the convex world, initial conditions matter. Consider the Alice-Bob problem above with the convex productivity curve, and the fact that splitting the training equitably is the worst possible solution. Management would ideally recognize Alice’s slight superiority and give her all the training, thus finding the optimal “edge case”. But what if Bob managed (convex dishonesty) to convince management that he was slightly superior to Alice and at, say, x = 0.2? Then Bob would get all the training, and Alice would get none, and management would converge on a sub-optimal local maximum. That is the essence of corporate backstabbing, is it not? Management’s increasing awareness of convexity in intellectual work means that it will tend to double down its investment in winners and toss away (fire) the losers. Thus, subordinates put considerable effort into creating the appearance of high potential for the sake of driving management to a local maximum that, if not necessarily ideal for the company, benefits them. That’s what “multiple local optima” means, in practical terms.

The traditional three-tiered corporation has a firm distinction between executives and managers (the third tier being “workers”, who are treated as a landscape feature) and its pertains to this. Because business problems are never entirely concave and orderly, the local “hill climbing” is left to managers, while the convex problems (which, like choosing initial conditions, require non-local insight) such as selecting leaders and business models are left to executives.

Yet with everything concave being performed, or soon to be performed, by machines, we’re seeing convexity pop up everywhere. The question of which programming languages to learn is a convex decision that non-managerial software engineers have to make in their careers. Picking a specialty is likewise; convexity is why it’s of value to specialize. The most talented people today are becoming self-executive, which means that they take responsibility for non-local matters that would otherwise be left to executives, including the direction of their own career. This, however, leads to conflicts with authority.

Older managers often complain about Millennial self-executivity and call it an attitude of entitlement. Actually, it’s the opposite. It’s disentitlement. When you’re entitled, you assume social contracts with other people and become angry when (from your perception) they don’t hold up their end. Millennials leave jobs, and furtively use slow periods to invest in their careers (e.g. in MOOCs) rather than asking for more work. That’s not an act of aggression or disillusion; it’s because they don’t believe the social contract ever existed. It’s not that they’re going to whine about a boss who doesn’t invest in their career– that would be entitlement– because that would do no good. They just leave. They weren’t owed anything, and they don’t owe anything. That’s disentitlement.

Convexity is bad for your job security

Here’s some scary news. When it comes to convex labor, most people shouldn’t be employed. First, let me show a concave input-output graph for worker productivity, assuming even distribution in worker ability from -1.0 to 1.0. Our model also assumes this ability statistic to be inflexible; there’s no training effect.

The blue line, at 82.44, represents the mean worker in the population. Why’s this important? It represents the expected productivity of a new hire off the street. If you’re at the median (x = 0.0) or even a bit below it, you are “above average”. It’s better to retain you than to bring someone in off the street. Let’s say that John is 40th percentile (x = -0.2) hire, which means that his productivity is 90. A random person hired off the street will be better than John, 60% of the time. However, the upside is limited (10 points at most) and the downside (possibly 70 points) is immense so, on average, it’s a terrible trade. It’s better to keep John (a known mediocre worker) on board than to replace him.

With a convex example, we find the opposite to be true:

Here, we have an arrangement in which most people are below the mean, so we’d expect high turnover. Management, one expects, would be inclined to hire people on a “try out” basis with the intention of throwing most of them back on the street. An average or even good (x = 0.5) hire should be thrown out in order to “roll the dice” with a new hire who might be the next star. Is that how managers actually behave? No, because there are frictional and morale reasons not to fire 80% of your people, and because this model’s assumption that people are inflexibly set at a competence level is not entirely true for most jobs, and those where it is true (e.g. fashion modeling) make it easy for management to evaluate someone before a hire is made. In-house experience matters. That is, however, how venture capital, publishing and record labels work. Once you turn out a couple failures, with those being the norm, it might still be that you’re a high performer who’s been unlucky, but you’re judged inferior to a random new entrant (with more upside potential) and flushed out of the system.

In the real world, it’s not so severe. We don’t see 80% of people being fired, and the reason is that, for most jobs, learning matters. The above applies to work at which there’s no learning process, but each worker is inflexibly put at a certain perfectly measurable productivity level. That’s not how the world really works. In-born talent is one relevant input, but there are others like skill, in-house experience, and education that have defensive properties and keep a person’s job security. People can often get themselves above the mean with hard work.

Secondly, the model above assumes workers are paid equally, which is not the case for most convex work. In the convex model above, the star (x = 1.0) might command several times the salary of the average performer (x = 0.0) and he should. That compensation inequality actually creates job security for the rest of them. If the best people didn’t charge more for their work, then employers would be inclined to fire middling performers in the search of a bargain.

This may be one of the reasons why there is such high turnover in the software industry. You can’t a get seasoned options trader for under $250,000 per year, but you can get excellent programmers (who are worth 5-10 times that amount, if given the right kind of work) for less than half of that. This is often individually justified (by the engineer) with an attitude of, “well, I don’t need to be paid millions; I care more about interesting work”. As an individual behavior, that’s fine, but it might be why so many software employers are so quick to toss engineers aside for dubious reasons. Once the manager concludes that the individual doesn’t have “star” potential, it’s worth it to throw out even a good engineer and try again for a shot at a bargain, considering the number of great engineers at mediocre salary levels.

One thing I’ve noticed in software (which is highly convex) is that there’s a cavalier attitude toward firing, and it’s almost certainly related to that “star economy” effect. What’s different is that software convexity has a lot inputs other that personal ability– project/person fit, tool familiarity, team cohesion, and a lot factors that are so hard to detect that they feel like pure luck– in the mix, so the “toss aside all but the best” strategy is severely defective, at least for a larger organization that should be enabling people to find better fitting projects, which makes a lot of sense amid convexity. That’s one of the reasons why I am so dogmatic about open allocation, at least in big companies.

Convexity is risky

Job insecurity amid convexity is an obvious problem, but not damning. If there’s a fixed demand for widgets, a competitor who can produce 10 times more of them is terrifying, because it will crash prices and put everyone else out of business (and, then, become a monopolist and raise them). Call that “red ocean convexity”, where the winners put the losers out of business because a “10X” performer takes 9X from someone else. However, if demand is limitless, then the presence of superior players isn’t always a bad thing. A movie star making $3 million isn’t ruined by one making $40 million. The arts are an example of “blue ocean convexity”, insofar as successful artists don’t make the others poorer, but increase the aggregate demand of art. It’s not “winner-take-all” insofar as one doesn’t have to be the top player to add something people value.

Computational problem solving (not “programming”) is a field where there’s very high demand, so the fact that top performers will produce an order of magnitude more value (the “10X effect”) doesn’t put the rest out of business. That’s a very good thing, because most of those top performers were among “the rest” when they started their career. Not only is there little direct competition, but as software engineers, we tend to admire those “10X” people and take every opportunity we can get to learn from them. If there were more of them, it wouldn’t make us poorer. It would make the world richer.

Is demand for anything limitless, though? For industrial products, no. Demand for televisions, for example, is limited by peoples’ need for them and space to put them. For making peoples’ lives better, yes. For improving processes, sure. Generation of true wealth (as Paul Graham defines it: “stuff people want”) is something for which there’s infinite demand, at least as far as we can see. So what’s the limiting factor? Why can’t everyone work on blue-ocean convex work that makes peoples’ lives better? It comes down to risk. So, let’s look at that. The model I’m going to use is as follows:

  • We only care about the immediate neighborhood of a specific (“typical”) competence level. We’ll call it x = 0.
  • Tasks have a difficulty t between -1.0 and 2.0, which represents the C in the logistic form. B is going to be a constant 4.5; just ignore that. 
  • The harder a task is, the higher the potential payoff. Thus, I’ll set A = 100 * (1 + e^(5*t)). This means that work gets more valuable slightly faster (11% faster) than it gets harder (“risk premium”). The constant term in A is based on the understanding that even very easy (difficulty of -1.0) work has value insofar as it’s time-consuming and therefore people must be paid to do it.
  • We measure risk for a given difficulty t by taking the first derivative of L(x; …), with respect to x, at x = 0. Why? L’(x; …) tells us how sensitive the output (payoff) is to marginal changes in input. We’re modeling unknown input variables and plain luck factors as a random, zero-mean “noise” variable d and assuming that for known competence x the true performance will be L(x + d; …). So this first derivative tells us, at x = 0, how sensitive we are to that unknown noise factor.

What we want to do is assess the yield (expected value) and risk (first derivative of yield) for difficulty levels from -1 to 2 when known x = 0. Here’s a graph of expected yield:

It’s hard to notice on that graph, but there’s actually a slight “dip” or “uncanny valley” as one goes from the extreme of easiness (t = -1.0) to slightly harder (-1.0 < t < 0.0) work:

Does it actually work that way in the real world? I have no idea. What causes this in the model is that, as we go from the ridiculously easy (t = 1.0) to the merely moderately easy (t = 0.5) the rate of potential failure grows faster than the maximum potential A does, as a function of t. That’s an artifact of how I modeled this and I don’t know for sure that a real-world market would have this trait. Actually, I doubt it would. It’s a small dip so I’m not going to worry about it. What we do see is that our yield is approximately constant as a function of difficulty for t from -1.0 to 0.0, where the work is concave for that level of skill; and then it grows exponentially as a function of t from 0.0 to 2.0, where the work is convex. That is what we tend to see on markets. The maximal market value of work (1 + e^(5 * t) in this model) grows slightly faster than difficulty in completing it (1 + e^(4.5*t), here).

However, what we’re interested in is risk, so let me show that as well by graphing the first derivative of L with respect to x (not t!) for each t.

What this shows us, pretty clearly, is monotonic risk increase as the tasks become more difficult. That’s probably not too surprising, but it’s nice to see what it looks like on paper. Notice that the easy work has almost no risk involved. Let’s plot these together. I’ve taken the liberty of normalizing the risk formula (in purple) to plot them together, which is reasonable because our units are abstract:

Let’s look at one other statistic, which will be the ratio between yield and risk. In finance, this is called the Sharpe Ratio. Because the units are abstract (i.e. there’s no real meaning to “1 unit” of competence or difficulty) there is no intrinsic meaning to its scale, and therefore I’ve again taken the liberty of normalizing this as well. That ratio, as a function of task difficulty, looks like this…

…which looks exactly like affine exponential decay. In fact, that’s what it is. The Sharpe Ratio is exponentially favorable for easy work (t < 0.0) and approaches a constant value (1.0 here, because of the normalization) for large t.

What’s the meaning of all this? Well, traditionally, the industrial problem was to maximize yield on capital within a finite “risk budget”. If that’s the case– you’re constrained by some finite amount of risk– then you want to select work according to the Sharpe Ratio. Concave tasks might have less yield, but they’re so low in risk that you can do more of them. For each quantum of risk in your budget, you want to get the most yield (expected value) out of it that you can. This favors the extreme concave labor. This is why industrial labor, for the past 200 years, has been almost all concave. Boring. Reliable. In many ways, the world still is concave and that’s a desirable thing. Good enough is good enough. However, it just so happens that when we, as humans, master a concave task when tend to look for the convex challenge of making it run itself. In pre-technological times, this was done by giving instructions to other people, and making machines as easy as possible for humans to use. In the technological era, it’s done with computers and code. Even the grunt work of coding is given to programs (we call them compilers) so we can focus on the interesting stuff. We’re programming all of that concave work out of human hands. Yes, concave work is still the backbone of the industrial world and always will be. It’s just not going to require humans doing it.

What if, instead, the risk budget weren’t an issue? Let’s say that we have a team of 5 programmers given a year to do whatever they want, and the worst they can do is waste their time, and you’re okay with that maximal-risk outcome (5 annual salaries for a learning experience). They might build something amazing that sells for $100 million, or they might work for a year and have the project still fail on the market. Maybe they do great work, but no one wants it; that’s a risk of creation. In this case, we’re not constrained by risk allocation but by talent. We’ve already accepted the worst possible outcome as acceptable. We want them to be doing convex work, which has the highest yield. Those top-notch people are the limiting resource, not risk allocation.

Convexity requires teamwork

Above, I established that if individual productivity is a convex function of investment in that person, and group performance is a sum of individual productivity, then the optimal solution is to ply one person with resources and starve (and likely fire) the rest. Is that how things actually work? No, not usually. There’s a glaring false assumption, which is the additive model where group performance is a simple sum of individual performances. Real team efforts shouldn’t work that way.

When a team is properly configured, most of their efforts don’t merely add to some pile of assets, but they multiply each others’ productivity. Each works to make the others more successful. I wrote about this advancement of technical maturity (from multiplier to adder) as it pertains to software but I think it’s more general. Warning: incompetent attempts at multiplier efforts are every bit as toxic as incompetent management and will have a divider effect.

Team convexity is a bit unique in the sense that both sides of the logistic “S-curve” are observed. You have synergy (convexity) as the team scales up to a certain size, but congestion (concavity) beyond a certain point. It’s very hard to get team size and configuration right, and typical “Theory Z” management (which attempts to coerce a heterogeneous set of people who didn’t choose each other, and probably didn’t choose the project, into being a team) generally fails at this. It can’t be managed competently from a top-down perspective, despite what many executives say (they are wrong). It has to be grass-roots self-organization. Top-down, closed-allocation management can work well in the Alice/Bob models above where productivity is the sum of individual performances (i.e. team synergies aren’t important) but it fails catastrophically on projects that require interactive, multiplicative effects in order to be successful.

Convexity has different rules

The technological economy is going to be very different, because of the way business problems are formulated. In the industrial economy, capital was held in some fixed amount by a business, whose goal was to gain as much yield (profit or interest) from it while keeping risk within certain bounds deemed acceptable. That made concavity desirable. It still is; stable income with low variation is always a good thing. It’s just that such work no longer requires humans. Concave work has been so commoditized that it’s hard to get a passive profit from it.

Ultimately, I think a basic income is the only way society will be able to handle widespread convexity of individual labor. What does it say about the future? People will either be very highly compensated, or effectively unemployed. There will be an increasing need for unpaid learning while people push themselves from the low, flat region of a convex curve to the high, steep part. Right now, we have a society where people with the means to indulge in that can put themselves on a strong career track, but the majority who have a lifelong need for monthly income end up getting shafted: they become a permanent class of unskilled labor and, by keeping wages low, they actually hold back technological advancement.

Industrial management was risk-reductive. A manager took ownership of some process and his job was to look for ways it could fail, then tried to reduce the sources of error in that process. The rare convex task (choosing a business strategy) was for a higher order of being, an executive. Technological management has to embrace risk, because all the concave work’s being taken by machines. In the future, it will only be economical for a human to do something when perfect completion is unknown or undefinable, and that’s the convex work.

A couple more graphs deserve attention, because both pertain to managerial goals. There are two ways that a manager can create a profit. One is to improve output. The other is to reduce costs. Which is favorable? It depends. Below is a graph that shows productivity ($/hour) as a function of wages for some task where performance is assumed to be convex in wages. The relationship is assumed here to be inflexible and go both ways: better people will expect more in wages, low wages will cause peoples’ out-of-work distractions to degrade their performance. Plotted in purple is the y = x or “break-even” line.

As one can see, it doesn’t even make sense to hire people for this kind of work at less than $68/hour: they’ll produce less than they cost. That “dip” is an inherent problem for convex work. Who’s going to pay people in the $50/hour range so they can become good and eventually move to the $100/hour range (where they’re producing $200/hour work)? This naturally tends toward a “winners and losers” scenario. The people who can quickly get themselves to the $70/hour productivity level (through the unpaid acquisition of skill) are employable, and will continue to grow; the rest will not be able to justify wages that sustain them. The short version: it’s hard to get into convex work.

Here’s a similar graph for concave work:

… and here’s a graph of the difference between productivity and wage, or per-hour profit, on each worker:

So the optimal profit is achieved at $24.45 per hour, where the worker provides $56.33 worth of work in that time. It doesn’t seem fair, but improvements to wages beyond that, while they improve productivity, do not improve it by enough to justify the additional cost. That’s not to say that companies will necessarily set wages to that level. (They might raise them higher to attract more workers, increasing total profit.) Also, here is a case where labor unions can be powerful (they aren’t especially helpful with convex work): in the above, the company would still earn a respectable profit on each worker with wages as high as $55 per hour, and wouldn’t be put out of business (despite managements’ claim that “you’ll break us” at, say, $40) until almost $80.

The tendency of corporate management toward cost-cutting, “always say no”, and Theory-X practices is an artifact of the above result of concavity. So while I can argue that “convexity is unfair” insofar as it encourages inequality of investment and resources, enabling small differences in initial conditions to produce a winner-take-all outcome; concavity produces its own variety of unfairness, since it often encourages wages to go to a very low level, where employers take a massive surplus.

The most important problem…?

Above is a lot about convexity, but I feel like the changeover to convexity in individual labor is the most important economic issue of the 21st century. So if we want to understand why the contemporary, MacLeod-hierarchical, organization won’t survive it, we need a deep understanding of what convexity is and how it works. I think we have that, now.

What does this have to do with Work Sucking? Well, there are a few things we get out of it. First, for the concave work that most of the labor force is still doing…

  • Concave (“commodity”) labor leads to grossly unfair wages. This creates a natural adversity between workers and management on the issue of wage levels. 
  • Management has a natural desire to reduce risk and cut costs, on an assumption of concavity. It’s what they’ve been doing for over 200 years. When you manage concave work, that’s the most profitable thing to do.
  • Management will often take a convex endeavor (e.g. computer programming) and try to treat it as concave. That’s what we, in software, call the “commodity developer” culture that clueless software managers try to shove down hapless engineers’ throats.
  • Stable, concave work is disappearing. Machines are taking it over. This isn’t a bad thing (on the contrary, it’s quite good) but it is eroding the semi-skilled labor base that gave the developed world a large middle class.

Now, for the convex:

  • Convex work favors low employment and volatile compensation. It’s not true that there “isn’t a lot of convex work” to go around. In fact, there’s a limitless amount of demand for it. However, one has to be unusually good for a company to justify paying for it at a level one could live on, because of the risk. Without a basic income in place, convexity will generate an economy where income volatility is at a level beyond what people are able to accept. As a firm believer in the need for market economies, this must be addressed.
  • Convex payoffs produce multiple optima on personnel matters (e.g. training, leadership). This sounds harmless until one realizes that “multiple optima” is a euphemism for “office politics”. It means there isn’t a clear meritocracy, as performance is highly context-sensitive.
  • Convex work often creates a tension between individual competition and teamwork. Managers attempting to grade individuals in isolation will create a competitive focus on individual productivity, because convexity rewards acceleration of small individual differences. This managerial style works for simple additive convexity, but fails in an organization that needs people to have multiplicative or synergistic effects (team convexity) and that’s most of them.

Red and blue ocean convexity

One of the surprising traits of convexity, tied-in with the matter of teamwork, is that it’s hard to predict whether it will be structurally cooperative or competitive. This leads me to believe that there are fundamental differences between “red ocean” and “blue ocean” varieties of convexity. For those unfamiliar with the terms, red ocean refers to well-established territory in which competition is fierce. There’s a known high quantity of resources (“blood in the water”) available but there’s a frenzy of people (some with considerable competitive advantages) working to get at it. It’s fierce and if you aren’t strong, the better predators will crowd you out. Blue ocean refers to unexplored territory where the yields are unknown but the competition’s less fierce (for now).

I don’t know this industry well, but I would think that modeling is an example of red-ocean convexity. Small differences in input (physical attractiveness, and skill at self-marketing) result in massive discrepancies of output, but there’s a small and limited amount of demand for the work. If there’s a new “10X model” on the scene, all the other models are worse off, because the supermodel takes up all of the work. For example, I know that some ridiculous percentage of the world’s hand-modeling is performed by one woman (who cannot live a normal life, due to her need to protect her hands).

What about professional sports, the distilled essence of competition? Blue ocean. Yep. That might seem surprising, given that these people often seem to want to kill each other, but the economic goal of a sports team is not to win games, but to play great games that people will pay money to watch. A “10X” player might revitalize the reputation of the sport, as Tiger Woods did for golf, and expand the audience. Top players actually make a lot of money for the opponents they defeat; the stars get a larger share of the pool, meaning their opponents get a smaller percentage, but they also expand that pool so much that everyone gets richer.

How about the VC-funded startup ecosystem? That’s less clear. Business formation is blue ocean convexity, insofar as there are plenty of untapped opportunities to add immense value, and they exist all over the world. However, fund-raising (at least, in the current investor climate) and press-whoring are red ocean convexity: a few already-established (and complacent) players get the lion’s share of the attention and resources, giving them an enormous head start. Indeed, this is the point of venture capital in the consumer-web space: use the “rocket fuel” (capital infusion) to take a first-entrant advantage before anyone else has a shot.

Red and blue ocean convexity are dramatically different in how they encourage people to think. With red-ocean convexity, it’s truly a ruthless, winner-take-all, space because the superior, 10X, player will force the others out of business. You must either beat him or join him. I recommend “join”. With blue-ocean convexity (which is the force that drives economic growth) outsized success doesn’t come at the expense of other people. In fact, the relationship may be symbiotic and cooperative. For example, great programmers build tools that are used all over the world and make everyone better at their jobs. So while there is a lot of inequality in payoffs– Linus Torvalds makes millions per year, I use his tools– because that’s how convexity works, it’s not necessarily a bad thing because everyone can win.

Convexity and progress

Convexity’s most important property is progressive time. Real-world convexity curves are often steeper than the ones graphed above and, if there isn’t a role for learning, then the vast majority of people will be unable to achieve at a level supporting an income, and thus unemployed. For example, while practice is key in (highly convex) professional sports, there aren’t many people who have the natural talent to earn a living at it. Convexity shuts out those without natural talent. Luckily for us and the world, most convex work isn’t so heavily influenced by natural limitations, but by skills, specialization and education. There’s still an elite at the rightward side of the payoff distribution curve that takes the bulk of the reward, but it’s possible for a diligent and motivated person to enter that elite by gaining the requisite skills. In other words, most of the inputs into that convex payoff function are within the individual actor’s control. This is another case of “good inequality”. In blue-ocean convexity, we want the top players to reap very large rewards, because it motivates more people to do the work that gets them there. 

Consider software engineering, which is perhaps the platonic ideal of blue-ocean convexity. What retards us the most as an industry is the lack of highly-skilled people. As an industry, we contend with managerial environments tailored to mediocrity, and suffer from code-quality problems that can reduce a technical asset’s real value to 80, 20, or even minus-300 cents on the dollar compared to its book value. Good software engineers are rare, and that hurts everyone. In fact, perhaps the easiest way to add $1 trillion in value to the economy would be to increase software engineer autonomy. Because most software engineers never get the environment of autonomy that would enable them to get any good, the whole economy suffers. What’s the antidote? A lot of training and effort– the so-called “10000 hours” of deliberate practice– that’s generally unpaid in this era of short-term, disposable jobs.

Convexity’s fundamental problem is that it requires highly-skilled labor, but no employer is willing to pay for people to develop the relevant skills, out of a fear that employees who drive up their market value will leave. In the short term, it’s an effective business strategy to hire mediocre “commodity developers” and staff them on gigantic teams for uninspiring projects, and give them work that requires minimal intellectual ability aside from following orders. In the long term, those developers never improve and produce garbage software that no one knows how to maintain, producing creeping morale decay and, sometimes, “time bombs” that cause huge business losses at unknown times in the future.

That’s why convexity is such a major threat to the full-employment society to which even liberal Americans still cling. Firms almost never invest in their people– empirically, we see that– in favor of the short-term “solution”, which is to ignore convexity and try to beat the labor context into concavity, that is terrible in the long term. Thus, even in convex work, the bulk of people linger at the low-yield leftward end of the curve. Their employers don’t invest in them, and often they lack the time and resources to invest in themselves. What we have, instead of blue-ocean convexity, is an economy where the privileged (who can afford unpaid time for learning) become superior because they have the capital to invest in themselves, and the rest are ignored and fall into low-yield commodity work. This was socially stable when there was a lot of concave, commodity work for humans to do, but that’s increasingly not the case.

Someone is going to have to invest in the long term, and to pay for progress and training. Right now, privileged individuals do it for themselves and their progeny, but that’s not scalable and will not avert the social instability threatened by systemic, long-term unemployment.

Trust and convexity

As I’ve said, convexity isn’t only a property of the relationship between individual inputs (talent, motivation, effort, skill) and productivity, but also occurs in team endeavors. Teams can be synergistic, with peoples’ efforts interacting multiplicatively instead of additively. That’s a very good thing, when it happens.

So it’s no surprise that large accomplishments often require multiple people. We already knew that! That is less true in 2013 than it was 1985– now, a single person can build a website serving millions– but it’s still the case. Arguably, it’s more the case now; it’s only that many markets have become so efficient that interpersonal dependencies “just work” and give more leverage to single actors. (The web entrepreneur is using technologies and infrastructure built by millions of other people.) At any rate, it’s only a small space of important projects that will be accomplished well by a single party, acting alone. For most, there’s a need to bring multiple people together, but to retain focus and that requires interior political inequalities (leadership) to the group.

We’re hard-wired to understand this. As humans, we fundamentally get the need for team endeavors with strong leadership. That’s why we enjoy team sports so much.

Historically, there have been three “sources of power” that have enabled people to undertake and lead large projects (team convexity):

  • coercion, which exists when negative consequences are used to motivate someone to do work that she wouldn’t otherwise do. This was the cornerstone of pre-industrial economies (slavery) but is also used, in a softer form, by ineffective managers: do this or lose your income/reputation. Anyway, coercion is how the Egyptian pyramids were built: coercive slave labor.
  • divination, in which leaders are elected based on an abstract principle, which may be the whim of a god, legal precedent, or pure random luck. For example, it has been argued that gambling (a case of “pure random luck”) served a socially positive purpose on the American frontier. Although it moved funds “randomly”, it allowed pools of capital to form, financing infrastructural ventures. Something like divination is how the cathedrals were built: voluntary labor, motivated by religious belief, directed by architects who often were connected with the Church. Self-divination, which tends to occur in a pure power vacuum, is called arrogation.
  • aggregation, where an attempt to compute, fairly, the group preference or the true market value of an asset is made. Political elections and financial markets are aggregations. Aggregation is how the Internet was built: self-directed labor driven by market forces.

When possible, fair aggregations are the most desirable, but it’s non-trivial to define what fair is. Should corporate management be driven by the one-dollar, one-vote system that exists today? Personally, I don’t think so. I think it sucks. I think employees deserve a vote simply because they have an obvious stake in the company. As much as the current, right-wing, state of the American electorate infuriates me, I really like the fact that citizens have the power to fire bad politicians. (They don’t use it enough; incumbent victory rates are so high that a bad politician has more job security than a good programmer.) Working people should have the same power over their management. By accepting a wage that is lower than the value of what they produce, they are paying their bosses. They have a right to dictate how they are managed, and to insist on the mentorship and training that convexity is making essential.

Because it’s so hard to determine a fair aggregation in the general case, there’s always some room for divination and arrogation, or even coercion in extreme cases. For example, our Constitution is a case of (secular, well-informed) divination on the matter of how to build a principled, stable and rational government, but it sets up an aggregation that we use elect political leaders. Additionally, if a political leader were voted out of office but did not hand over power, he’d be pushed out of it by force (coercion). Trust is what enables self-organizing (or, at least, stable) divination. People will grant power to leaders based on abstract principles if they trust those ideas, and they’ll allow representatives to act on their behalf if they trust those people.

Needless to say, convex payoffs to group efforts generate an important role for trust. That’s what the “stone soup” parable is about; because there’s no trust in the community, people hoard their own produce instead of sharing, and no one has had a decent meal for months. When outside travelers offer a nonexistent delicacy– the stone is a social catalyst with no nutritional value– and convince the other villagers to donate their spare produce, they enable them all to work together. So they get a nutritious bowl of soup and, one hopes, they can start to trust each other and build at least a barter or gift economy. They all benefit from the “stone soup”, but they were deceived.

Convex dishonesty isn’t always bad. It is the act of “borrowing” trust by lying to people, with the intent to pay them back out of the synergistic profits. Sometimes convex dishonesty is exactly what a person needs to do in order to get something accomplished. Nor is it always good. Failed convex frauds are damaging to morale, and therefore they often exacerbate the lack-of-trust problem. Moreover, there are many endeavors (e.g. pyramid schemes) that have the flavor of convex fraud but are, in reality, just fraud.

This, in fact, is why modern finance exists. It’s to replace the self-divinations that pre-financial societies required to get convex projects done with a fairer aggregation system that properly measures, and allows the transfer of, risks.

Credibility

For macroscopic considerations like the fair prices of oil or business equity, financial aggregations seem to work. What about the micro-level concern of what each worker should do on a daily basis? That usually exists in the context of a corporation (closed system) with specific authority structures and needs. Companies often attempt to create internal markets (tough culture) for resources and support, with each team’s footprint measured in internal “funny money” given the name of dollars. I’ve seen how those work, and they often become corrupt. The matter of how people direct the use of their time is based on an internal social currency (including job titles, visibility, etc.) that I’ve taken to calling credibility. It’s supposed to create a meritocracy, insofar as the only way one is supposed to be able to get credibility is through hard work and genuine achievement, but it often has some severely anti-meritocratic effects. 

So why does your job (probably) Suck? Your job will generally suck if you lack credibility, because it means that you don’t control your own time, have little choice over what you do and how you do it, and that your job security is poor. Your efforts will be allocated, controlled, and evaluated by an external party (a manager) whose superiority in credibility grants him the right of self-divination. He gets to throw your time into his convex project, but not vice versa. You don’t have a say in it. Remember: he’s got credibility, and you lack it. 

Credibility always generates a black market. There is no failing in this principle. Performance reviews are gamed, with various trades being made wherein managers offer review points in exchange for non-performance-related favors (such as vocal support for an unrelated project, positive “360-degree reviews”, and various considerations that are just inappropriate and won’t be discussed here) and loyalty. Temporary strongmen/thugs use transient credibility (usually, from managerial favoritism) to intimidate and extort other people into sharing credit for work accomplished, thus enabling the thug to appear like a high performer and get promoted to a real managerial role (permanent credibility). You win on a credibility market by buying and selling it for a profit, creating various perverted social arbitrages. No organization that has allowed credibility to become a major force has avoided this.

Now I can discuss the hierarchy as immortalized by this cartoon from Hugh MacLeod:

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Losers are not undesirable, unpopular, or useless people. In fact, they’re often the opposite. What makes them “losers” is that, in an economic sense, they’re losing insofar as they contribute more to the organization than they get out of it. Why do they do this? They like the monthly income and social stability. Sociopaths (who are not bad people; they’re just gamblers) take the other side of that risk trade. They bear a disproportionate share of the organization’s risk and work the hardest, but they get the most reward. They have the most to lose. A Loser who gets fired will get another job at the same wage; a Sociopath CEO will have to apply for subordinate positions if the company fails. Clueless are a level that forms later on when this risk transfer becomes degenerate– the Sociopaths are no longer putting in more effort or taking more risk than anyone else, but have become an entitled, complacent rent-seeking class– and they need a middle-management layer of over-eager “useful idiots” to create the image (Effort Thermocline) that the top jobs are still demanding.

What’s missing in this analysis? Well, there’s nothing morally wrong, at all, with a financial risk transfer. If I had a resource that had a 50% chance of yielding $10 million, and 50% chance of being worthless, I’d probably sell it to a rich person (whose tolerance of risk is much greater) for $4.9 million to “lock in” that amount. A +5-million-dollar swing in personal wealth is huge to me and minuscule to him. It’d be a good trade for both of us. I’d be paying a (comparably small) $100,000 risk premium to have that volatility out of my financial life. I’m not a Loser in this deal, and he’s not a Sociopath. It’s by-the-book finance, how it’s supposed to work.

What generates the evil, then? Well, it’s the credibility market. I don’t hold the individual firm responsible for prevailing financial scarcity and, thus, the overwhelmingly large number of people willing to make low-expectancy plays. As long as that firms pays its people reasonably, it has clean hands. So the financial Loser trade is not a sign of malfeasance. The credibility market’s different, because the organization has control over it. It creates the damn thing. Thus, I think the character of the risk transfer has several phases, each deserving its own moral stance:

  1. Financial risk transfer. Entrepreneurs put capital and their reputations at risk to amass the resources necessary to start a project whose returns are (macroscopically, at least) convex. This pool of resources is used to pay bills and wages, therefore allowing workers to get a reliable, recurring monthly wage that is somewhat less than the expected value of their contribution. Again, there’s nothing morally wrong here. Workers are getting a risk-free income (so long as the business continues to exist) while participating in the profits of industrial macro-convexity. 
  2. De-risking, entrenchment, and convex fraud. As the business becomes more established, its people stop viewing it as a risk transfer between entrepreneurs and workers, and start seeing it (after the company’s success is obvious) as a pool of “free” resources to gain control over. Such resources are often economic (“this place has millions of dollars to fund my ideas”) but reputation (“imagine what I could do as a representative of X”) is also a factor. People begin making self-divination (convex fraud) gambits to establish themselves as top performers and vault into the increasingly complacent, rent-seeking, executive tier. This is a red-ocean feeding frenzy for the pile of surplus value that the organization’s success has created.
  3. Credibility emerges, and becomes the internal currency. Successful convex fraudsters are almost always people who weren’t part of the original founding team. They didn’t get their equity when it was cheap, so now they’re in an unstable positions. They’re high-ranking managers, but haven’t yet entwined themselves with the business or won a significant share of the rewards/equity. Knowing that their success is a direct output of self-divination (that is, arrogation) they use their purloined social standing to create official credibility in the forms of titles (public statements of credibility), closed allocation (credibility as a project-maker and priority-setter), and performance reviews (periodic credibility recalibrations). This turns the unofficial credibility they’ve stolen into an official, secure kind.
  4. Panic trading, and credibility risk transfer. Newly formed businesses, given their recent memory of existential risk, generally have a cavalier attitude toward firing and a tough culture, which I’ll explain below. This means that a person can be terminated not because of doing anything wrong or being incompetent, but just because of an unlucky break in credibility fluctuations (e.g. a sponsor who changes jobs, a performance-review “vitality curve”). In role-playing games, this is the “killed by the dice” question: should the GM (game coordinator who functions as a neutral party, creating and directing the game world) allow characters, played well, to die– really die, in the “create a new character” sense, not in the “miraculously resurrected by a level-18 healer” sense– because of bad rolls of the dice? In role-playing games, it’s a matter of taste. Some people hate games where they can lose a character by random chance; others like the tension that it creates. At work, though, “killed by the dice” is always bad. Tough-culture credibility markets allow good employees to be killed by the dice. In fact, when stack-ranking and “low performer” witch hunts set in, they encourage it. This creates a lot of panic trading and there’s a new risk transfer in town. It’s not the morally acceptable and socially-positive transfer of financial risk we saw in Stage 1. Rather, it’s the degenerate black-market credibility trading that enables the worst sorts of people (true psychopaths) to rise.
  5. Collapse into feudalistic rank culture. No one wants a job where she can be fired “for performance” because of bad luck, so tough cultures don’t last very wrong; they turn into rank cultures. People (Losers) panic-trade their credibility, and would rather subordinate to get some credibility (“protection”) from a feudal lord (Sociopath) than risk having none and being flushed out. The people who control the review process become very powerful and, eventually, can manufacture enough of an image of high performance to become official managers. You’re no longer going to be killed by the dice in a rank culture, but you can be killed by a manager because he can unilaterally reduce your credibility to zero.
  6. Macroscopic underperformance and decline. Full-on rank culture is terribly inefficient, because it generates so much fourth-quadrant work that serves the need of local extortionists (usually, middle managers and their favorites) but does not help the business. Eventually, this leads to underperformance of the business as a whole. Rank culture fosters so much incompetence that trust breaks down within the organization, and it’s often permanent. Firing bad apples is no longer possible, because the process of flushing them away would require firing a substantial fraction of the organization, and that would become so politicized and disruptive as to break the company outright. Such companies regularly lapse into brief episodes of “tough culture”, when new executives (usually, people who buy it as its market value tanks) decide that it’s time to flush out the low performers, but they usually do it in a heavy-handed, McKinsey-esque way that creates a new and equally toxic credibility market. But… like clockwork, those who control said black markets become the new holders of rank and, soon enough, the official bosses. These mid-level rank-holders start out as the mean-spirited witch-hunters (proto-Sociopaths) who implement the “low performer initiative” but they eventually rise and leave a residue of strategically-unaware, soft, complacent and generally harmless mid-ranking “useful idiots” (new Clueless). Clueless are the middle managers who get some power when the company lurches into a new rank culture, but don’t know how to use it and don’t know the main rule of the game of thrones: you win or you die.
  7. Obsolescence and death. Self-explanatory. Some combination of rank-culture complacency and tough-culture moral decay turn the company into a shell of what it once was. The bad guys have taken out their millions and are driving up house prices in the area and their wives with too much plastic surgery are on zoning committees keeping those prices high; everyone else who worked at the firm is properly fucked. Sell off the pieces that still have value, close the shop.

That cycle, in the industrial era, used to play out over decades. If you joined a company in Stage 1 in 1945, you might start to see the Stage 4 midlife when you retired in 1975. Now, it happens much more quickly: it goes down over years, and sometimes months for fast-changing startups. It’s much more of an immediate threat to personal job security than it has ever been before. Cultural decay used to be a long-term existential risk to companies not taken seriously because calamity was decades away; now, it’s often ongoing and rapid thanks to the “build to flip” mentality.

To tell the truth about it, the MacLeod rank culture wasn’t such a bad fit for the industrial era. Industrial enterprises had a minimal amount of convex work (choosing the business model, setting strategies) that could be delegated to a small, elite, executive nerve-center. Clueless middle managers and rationally-disengaged (Loser) wage earners could implement ideas delivered from the top without too much introspection or insight, and that was fine because individual work was concave. Additionally, that small set of executives could be kept close to the owners of the company (if they weren’t the same set of people).

In the technological era, individual labor is convex and we can no longer afford Cluelessness, or Loserism. The most important work– and within a century or so, all work where there’s demand for humans to do it– requires self-executivity. The hierarchical corporation is a brachiosaur sunning itself on the Yucatan, but that bright point of light isn’t the sun.

Your job is a call option

If companies seem to tolerate, at least passively, the inefficiency of full-blown rank culture, doesn’t that mean that there isn’t a lot of real work for them to do? Well, yes, that’s true. I’ve already discussed the existence of low-yield, boring, Fourth Quadrant busywork that serves little purpose to the business. It’s not without any value, but it doesn’t do much for a person’s career. Why does it exist? First, let’s answer this: where does it come from?

Companies have a jealously-guarded core of real work: essential to the business, great for the careers of those who do it. The winners of the credibility market get the First Quadrant (1Q) of interesting and essential work. They put themselves on the “fun stuff” that is also the core of the business– it’s enjoyable, and it makes a lot of money for the firm and therefore leads to high bonuses. There isn’t a lot of work like this, and it’s coveted, so few people can be in this set. Those are akin to feudal lords, and correspond with MacLeod Sociopaths. Those who wish to join their set, but haven’t amassed enough credibility yet, take on the less enjoyable, but still important Second Quadrant (2Q) of work: unpleasant but essential. Those are the vassals attempting to become lords in the future. That’s often a Clueless strategy because it rarely works, but sometimes it does. Then there is a third monastic category of people who have enough credibility (got into the business early, usually) to sustain themselves but have no wish to rise in the organizational hierarchy. They work on fun, R&D projects that aren’t in the direct line of business (but might be, in the future). They do what’s interesting to them, because they have enough credibility to get away with that and not be fired. They work on the Third Quadrant (3Q): interesting but discretionary. How they fit into the MacLeod pyramid is unclear. I’d say they’re a fortunate sub-caste of Losers in the sense that they rationally disengage from the power politics of the essential work; but they’re Clueless if they’re wrong about their job security and get fired. Finally, who gets the Fourth Quadrant (4Q) of unpleasant and discretionary work? The peasants. The Losers without the job security of permanent credibility are the ones who do that stuff, because they have no other choice.

Where does the Fourth Quadrant work come from? Clueless middle-managers who take undesirable (2Q) or unimportant (3Q) projects, but manage to take all the career upside (turning 2Q into 4Q for their reports) and fun work (turning 3Q into 4Q) for themselves, leaving their reports utterly hosed. This might seem to violate their Cluelessness; it’s more Sociopathic, right? Well, MacLeod “Clueless” doesn’t mean that they don’t know how to fend for themselves. It means they’re non-strategic, or that they rarely know what’s good for the business or what will succeed in the long-term. They suck at “the big picture” but they’re perfectly capable of local operations. Additionally, some Clueless are decent people; others are very clearly not. It is perfectly possible to be MacLeod Clueless and also a sociopath.

Why do the Sociopaths in charge allow the blind Clueless to generate so much garbage make-work? The answer is that such work is evaluative. The point of the years-long “dues paying” period is to figure out who the “team players” are so that, when leadership opportunities or chances for legitimate, important work open up, the Sociopaths know which of the Clueless and Losers to pick. In other words, hiring a Loser subordinate and putting him on unimportant work is a call option on a key hire, later.

Workplace cultures

I mentioned rank and tough cultures above, so let me get into more detail of what those are. In general, an organization is going to evaluate its individuals based on three core traits:

  • subordinacy: does this person put the goals of the organization (or, at least, his immediate team and supervisor) above her own?
  • dedication: will she do unpleasant work, or large amounts of work, in order to succeed?
  • strategy: does she know what is worth working on, and direct her efforts toward important things?

People who lack two or all three of these core traits are generally so dysfunctional that all but the most nonselective employers just flush them out. Those types– such as the strategic, not-dedicated, and insubordinate Passive-Aggressive and the dedicated, insubordinate, and not-strategic Loose Cannon– occasionally pop up for comic relief, but they’re so incompetent that they don’t last long in a company and are never in contention for important roles. I call them, as a group, the Lumpenlosers.

MacLeod Losers tend to be strategic and subordinate, but not dedicated. They know what’s worth working on, but they tend to follow orders because they’re optimizing for comfort, social approval, and job security. They don’t see any value in 90-hour weeks (which would compromise their social polish) or radical pursuit of improvement (which would upset authority). They just want to be liked and adjust well to the cozy, boring, middle-bottom. If you make a MacLeod Loser work Saturdays, though, she’ll quit. She knows that she can get a similar or better job elsewhere.

MacLeod Clueless are subordinate and dedicated but not strategic. They have no clue what’s worth working on. They blindly follow orders, but will also put in above-board effort because of an unconditional work ethic. They frequently end up cleaning up messes made by Sociopaths above and Losers below them. They tend to be where the corporate buck actually stops, because Sociopaths can count on them to be loyal fall guys.

MacLeod Sociopaths are dedicated and strategic but insubordinate. They figure out how the system works and what is worth putting effort into, and they optimize for personal yield. They’re risk-takers who don’t mind taking the chance of getting fired if there’s also a decent likelihood of a promotion. They tend to have “up-or-out” career trajectories, and job hopping isn’t uncommon.

Since there are good Sociopaths out there, I’ve taken to calling the socially positive ones the Technocrats, who tend to be insubordinate with respect to immediate organizational authority, but have higher moral principles rooted in convexity: process improvements, teamwork and cooperation, technical and infrastructural excellence. They’re the “positive-sum” radicals.  I’ll get back to them.

Is there a “unicorn” employee who combines all three desired traits– subordinacy, dedication, and strategy? Yes, but it’s strictly conditional upon a particular set of circumstances. In general, it’s not strategic to be subordinate and dedicated. If you’re strategic, you’ll usually either optimize for comfort and be subordinate, but not dedicated, because that’s uncomfortable. If you follow orders, it’s pretty easy to coast in most companies. That’s the Loser strategy. Or, you might optimize for personal yield and work a bit harder, becoming dedicated, but you won’t do it for a manager’s benefit: it’s either your own, or some kind of higher purpose. That’s the Sociopath strategy. The exception is a mentor/protege relationship. Strategic and dedicated people will subordinate if they think that the person in authority knows more than they do, and is looking out for their career interests. They’re subordinating to a mentor conditionally, based on the understanding that they will be in authority, or at least able to do more interesting and important work, in the future.

From this understanding, we can derive four common workplace cultures:

  • rank cultures value subordinacy above all. You can coast if you’re in good graces with your manager, and the company ultimately becomes lazy. Rank cultures have the most pronounced MacLeod pyramid: lazy but affable Losers, blind but eager Clueless, and Sociopaths at the top looking for ways to gain from the whole mess. 
  • tough cultures value dedication, and flush out the less dedicated using informal social pressure and formal performance reviews. It’s no longer acceptable to work a standard workweek; 60 hours is the new 40. Tough culture exists to purge the Loser tier, splitting it between the neo-Clueless sector and the still-Loser rejects, which it will fire if they don’t quit first. So the MacLeod pyramid of a tough culture is more fluid, but every bit as pathological.
  • self-executive cultures value strategy. Employees are individually responsible for directing their own efforts into pursuits that are of the most value. This is the open allocation for which Valve and Github are known. Instead of employees having to compete for projects (tough culture) or managerial support (rank culture) it is the opposite. Projects compete for talent on an open market, and managers (if they exist) must operate in the interests of those being managed. There is no MacLeod hierarchy in a self-executive culture.
  • guild culture values a balance of the three. Junior employees aren’t treated as terminal subordinates but as proteges who will eventually rise into leadership/mentoring positions. There isn’t a MacLeod pyramid here; to the extent that there may be undesirable structure, it has more to do with inaccurate seniority metrics (e.g. years of experience) than with bad-faith credibility trading. 

Rank and guild cultures are both command cultures, insofar as they rely on central planning and global (within the institution) rule-setting. Top management must keep continual awareness of how many people are at each level, and plan out the future accordingly. Tough and self-executive cultures are market cultures, because they require direct engagement with an organic, internal market.

The healthy, “Theory Y” cultures are the guild and self-executive cultures. These confer a basic credibility on all employees, which shuts off the panic trading that generates the MacLeod process. In a guild culture, each employee has credibility for being a student who will grow in the future. In self-executive culture, each employee has power inherent in the right to direct her efforts to the project she considers most worthy. Bosses and projects competing for workers is a Good Thing. 

The pathological, “Theory X” cultures are the rank and tough cultures. It goes without saying that most rank cultures try to present themselves as guild cultures– but management has so much power that it need not take any mentorship commitments seriously. Likewise, most tough cultures present themselves as self-executive ones. How do you tell if your company has a genuinely healthy (Theory Y) culture? Basic credibility. If it’s there, it’s the good kind. If it’s not, it’s the bad kind of culture.

Basic credibility

In a healthy company, employees won’t be “killed by the dice”. Sure, random fluctuations in credibility and performance might delay a promotion for a year or two, but the panicked credibility trading of the Theory-X culture isn’t there. People don’t fear their bosses in a Theory-Y culture; they’re self-motivated and fear not doing enough by their own standards– because they actually care. Basic credibility means that every employee is extended enough credibility to direct his own work and career.

That does not mean people are never fired. If someone punches a colleague in the face or steals from the company, you fire him, but it has nothing to do with credibility. You get rid of him because, well, he did something illegal and harmful. What it does mean is that people aren’t terminated for “performance reasons” that really mean either (a) they were just unlucky and couldn’t get enough support to save them in tough-culture “stack ranking”, or (b) their manager disliked them for some reason (no-fault lack-of-fit, or manager-fault lack-of-fit). It does mean that people are permitted to move around in the company, and that the firm might tolerate a real underperformer for a couple of years. Guess what? In a convex world, underperformance almost doesn’t matter.

With convexity, the difference between excellence and mediocrity matters much more than that between mediocrity and underperformance. In a concave world, yes, you must fire underperformers because the margin you get on good employees is so low that one slacker can cancel out 4 or 5 good people. In a convex world, the danger isn’t that you have a few underperformers. You will have, at the least, good-faith low-performers, just because the nature of convexity is to create risk and inequality of return and some peoples’ projects won’t pan out. Thjat’s fine. Instead, the danger is that you don’t have any excellent (“10x”) employees.

There’s a managerial myth that cracking down on “low performers” is useful because they demotivate the “10x-ers”. Yes and no. Incompetent management and having to work around bad code are devastating and will chase out your top performers. If 10xer’s have to work with incompetents and have no opportunity to improve them, they get frustrated and quit. There are toxic incompetents (dividers) who make others unproductive and damage morale, and then there are low-impact employees who just need more time (subtracters). Subtracters cost more in salary than they deliver, but they aren’t hurting anyone and they will usually improve. Fire dividers immediately. Give subtracters a few years (yes, I said years) to find a fit. Sometimes, you’ll hire someone good and still have that person end up as a subtracter at first. That common in the face of convexity– and remember that convexity is the defining problem of the 21st-century business world. The right thing to do is to let her keep looking for a fit until she finds one. Almost never will it take years if your company runs properly.

“Low performer initiatives” rarely smoke out the truly toxic dividers, as it turns out. Why? Because people who have defective personalities and hurt other peoples’ morale and productivity are used to having their jobs in jeopardy, and have learned to play politics. They will usually survive. It’ll be unlucky subtracters you end up firing. You might save chump change on the balance sheet, but you’re not going to fix the real organizational problems.

Theories X, Y, and Z

I grouped the negative workplace cultures (rank and tough) together and called them Theory X; the positive ones (self-executive and guild) I called Theory Y. This isn’t my terminology; it’s about 50 years old, coming from Douglas MacGregor. The 1960s was the height of Theory Y management, so that was the “good” managerial style. Let’s compare them and see what they say.

Recall what I said about the “sources of power”: coercion, divination, and aggregation. Coercion was, by far, the predominant force in aggregate labor before 1800. Slavery, prisons, and militaries (with, in that time, lots of conscription) were the inspirations for the original corporations, and the new class of industrialists was very cruel: criminal by modern standards. Theory X was the norm. Under Theory X, workers are just resources. They have no rights, no important desires, and should be well-treated only if there’s an immediate performance benefit. Today, we recognize that as brutal and psychotic, but for a humanity coming off over 100,000 years of male positional violence and coerced labor, the original-sin model of work shouldn’t seem far off. Theory X held that employees are intrinsically lazy and selfish and will only work hard if threatened.

Around 1920, industrialists began to realize that, even though labor in that time mostly was concave, it was good business to be decent to one’s workers. Henry Ford, a rabid anti-Semite, was hardly a decent human being, much less “a nice guy”, but even he was able to see this. He raised wages, creating a healthy consumer base for his products. He reduced the workday to ten hours, then eight. The long days just weren’t productive. Over the next forty years, employers learned that if workers were treated well, they’d repay the favor by behaving better and working harder. This lead to the Theory Y school of management, which held that people were intrinsically altruistic and earnest, and that management’s role was to nurture them. This gave birth to the paternalistic corporation and the bilateral social contracts that created the American middle class.

Theory Y failed. Why? It grew up in the 1940s to ’60s, when there was a prosperous middle class, but in a time of very low economic inequality. One thing that would amaze most Millennials is that, when our parents grew up, the idea that a person would work for money was socially unacceptable. You just couldn’t say that you wanted to get rich, in 1970, and not be despised for it. And it was very rare for a person to make 10 times more than the average citizen! However, the growth of economic inequality that began in the 1970s, and accelerated since then, raised the stakes. Then the Reagan Era hit.

Most of the buyout/private equity activity that happened in the 1980s had a source immortalized by the movie Wall Street: industrial espionage, mostly driven by younger people eager to sell out their employers’ secrets to get jobs from private equity firms. There was a decade of betrayal that brutalized the older, paternalistic corporations. Given, by a private equity tempter, the option of becoming CEO immediately through chicanery, instead of working toward it for 20 years, many took the former. Knives came out, backs were stabbed, and the most trusting corporations got screwed.

Since the dust settled, around 1995, the predominant managerial attitude has been Theory Z. Theory X isn’t socially acceptable, and Theory Y’s failure is still too recently remembered. What’s Theory Z? Theory X takes a pessimistic view of workers and distrusts everyone. Theory Y takes an optimistic view of human nature and becomes too trusting. Theory Z is the most realistic of the three: it assumes that people are indifferent to large organizations (even their employers) but loyal to those close to them (family, friends, immediate colleagues, distant co-workers; probably in that order). Human nature is neither egoistic or altruistic, but localistic. This was an improvement insofar as it holds a more realistic view of how people are. It’s still wrong, though.

What’s wrong with Theory Z? It’s teamist. Now, when you have genuine teamwork, that’s a great thing. You get synergy, multiplier effects, team convexity– whatever you want to call it, I think we all agree that it’s powerful. The problem with the Theory-Z company is that it tries to enforce team cohesion. Don’t hire older people; they might like different music! Buy a foosball table, because 9:30pm diversions are how creativity happens! This is more of a cargo cult than anything founded in reasonable business principles, and it’s generally ineffective. Teamism reduces diversity and makes it harder to bring in talent (which is critical, in a convex world). It also tends toward general mediocrity.

Each Theory had a root delusion in it. Theory X’s delusion was that morale didn’t matter; workers were just machines. Theory Y’s delusion is rooted in the tendency for “too good” people to think everyone else is as decent as they are; it fell when the 1980s made vapid elitism “sexy” again, and opportunities to make obscene wealth in betraying one’s employer emerged. Theory Z’s delusion is that a set of people who share nothing other than a common manager constitute a genuine (synergistic) team. See, in an open-allocation world, you’re likely to get team synergies because of the self-organization. People would naturally tend to form teams where they make each other more productive (multiplier effects). It happens at the grass-roots level, but can’t be forced in people who are deprived of autonomy. With closed-allocation, you don’t get that. People (with diverging interests) are brought together by force outside of their control and told to be a team. Closed-allocation Theory Z lives in denial of how rare those synergistic effects actually are.

I mentioned, previously an alternative to these 3 theories that I’ve called Theory A, which is a more sober and realistic slant on Theory Y: trust employees with their own time and energy; distrust those who want to control others. I’ll return to that in Part 22, the conclusion.

Morality, civility, and social acceptability

The MacLeod Sociopaths that run large organizations are a corrosive force, but what defines them isn’t true psychopathy, although some of them are that. There are also plenty of genuinely good people who fit the MacLeod Sociopath archetype. I am among them. What makes them dangerous is that the organization has no means to audit them. If it’s run by “good Sociopaths” (whom I’ve taken to calling Technocrats) then it will be a good organization. However, if it’s run by the bad kind, it will degenerate. So, with the so-called Sociopaths (while it is less necessary for the Losers and Clueless) it is important to understand the moral composition of that set.

I’ve put a lot of effort into defining good and evil, and that’s a big topic I don’t have much room for, so let me be brief on them. Good is motivated by concerns like compassion, social justice, honesty, and virtue. Evil is militant localism or selfishness. In an organizational context, or from a perspective of individual fitness, both are maladaptive when taken to the extreme. Extreme good is self-sacrifice and martyrdom that tends to take a person out of the gene pool, and certainly isn’t good for the bottom line; extreme evil is perverse sadism that actually gets in a person’s way, as opposed to the moderate psychopathy of corporate criminals.

Law and chaos are the extremes of a civil spectrum, which I cribbed from AD&D. Lawful people have faith in institutions and chaotic people tend to distrust them. Lawful good sees institutions as tending to be more just and fair than individual people; chaotic good finds them to be corrupt. Lawful neutrality sees institutions as being efficient and respectable; chaotic neutrality finds them inefficient and deserving of destruction. Lawful evil sees institutions as a magnifier of strength and admires their power; chaotic evil sees them as obstructions that get in the way of raw, human dominance. 

Morality and civil bias, in people, seem to be orthogonal. In the AD&D system, each spectrum has three levels, producing 9 alignments. I focused on the careers of each here. In reality, though, there’s a continuous spectrum. For now, I’m just going to assume a Gaussian distribution, mean 0 and standard deviation 1, with the two dimensions being uncorrelated.

MacLeod Losers tend to be civilly neutral, and Clueless tend to be lawful; but MacLeod Sociopaths come from all over the map. Why? To understand that, we need to focus on a concept that I call well-adjustment. To start, humans don’t actually value extremes in goodness or in law. Extreme good leads to martyrdom, and most people who are more than 3 standard deviations of good are taken to be neurotic narcissists, rather than being admired. Extremely lawful people tend to be rigid, conformist, and are therefore not much liked either. I contend that there’s a point of maximum well-adjustment that represents what our society says people are supposed to be. I’d put it somewhere in the ballpark of 1 standard deviation of good, and 1 of law, or the point (1, 1). If we use +x to represent law, -x to represent chaos, +y to represent good, and -y to represent evil, we get the well-adjustment formula:

Here, low f means that one is more well-adjusted. It’s better to be good than evil, and to be lawful than chaotic, but it’s best to be at (1, 1) exactly. But wait! Is there really a difference between (1, 1) and (0, 0)? Or between (5, 5) and (5, 6)? Not really, I don’t think. Well-adjustment tends to be a binary relationship, so I’m going to put f through a logistic transform where 0.0 means total ill-adjustment at 1.0 means well-adjustment. Middling values represent a “fringe” of people who will be well-adjusted in some circumstances but fail, socially speaking, in others. Based on my experience, I’d guess that this:

is a good estimate. If your squared distance from the point of maximal well-adjustment is less than 4, you’re good. If it’s more than 8, you’re probably ill-adjusted– too good, too evil, too lawful, or too chaotic. What gives us, in the 2-D moral/civil space, is a well-adjustment function looking exactly like this:

whose contours look like this:

Now, I don’t know whether the actual well-adjustment function that drives human social behavior has such a perfect circular shape. I doubt it does. It’s probably some kind of contiguous oval, though. The white part is a plateau of high (near 1.0) social adjustment. People in this space tend to get along with everyone. Or, if they have social problems, it has little to do with their moral or civil alignments, which are socially acceptable. The red outside is a deep sea (near 0.0) of social maladjustment. It turns out that if you’re 2 standard deviations of evil and of chaos, you have a hard time making friends.

In other words, we have a social adjustment function that’s almost binary, but there’s a really interesting circular fringe that produces well-adjustment values between 0.1 and 0.9. Why would that be important? Because that’s where the MacLeod Sociopaths comes from.

Well-adjusted people don’t rise in organizations. Why? Because organizations know exactly how to make it so that well-adjusted, normal people don’t mind being at the bottom, and will slightly prefer it if that’s where the organization thinks they belong. It’s like Brave New World, where the lower castes (e.g. Gammas) are convinced that they are happiest where they are. If you’re on that white plateau of well-adjustment, you’ll probably never be fired. You’ll always have friends wherever you go. You can get comfortable as a MacLeod Loser, or maybe Clueless. You don’t worry. You don’t feel a strong need to rise quickly in an orgnaization.

Of course, the extremely ill-adjusted people in the red don’t rise either. That should not surprise anyone. Unless they become very good at hiding their alignments, they are too dysfunctional to have a shot in social organizations like a modern corporation. To put it bluntly, no one likes them.

However, let’s say that a Technocrat has 1.25 standard deviations of law and chaos each, making her well-adjustment level 0.65. She’s clearly in that fringe category. What does this mean? It means that she’ll be socially acceptable in about 65% of all contexts. The MacLeod Loser career isn’t an option for her. She might get along with one set of managers and co-workers, but as they change, things may turn against her. Over time, something will break. This gives her a natural up-or-out impetus. If she doesn’t keep learning new things and advancing her career, she could be hosed. She’s liked by more people than dislike her, but she can’t rely on being well-liked as it were a given.

It’s people on the fringe who tend to rise to the top of, and run, organizations, because they can never get cozy on the bottom. We can graph “fringeness”, measured as the magnitude of the slope (derivative) of the well-adjustment function and you get contours like this:

It’s a ring-shaped fringe. Nothing too surprising. The perfection of the circular ring is, of course, an artifact of the model. I don’t know if it’s this neat in the real world, but the idea there is correct. Now, here’s where things get interesting. What does that picture tell us? Not that much aside from what we already know: the most ambitious (and, eventually, most successful) people in an organization will be those who are not so close to the “point of maximal well-adjustment” to get along in any context, but not so far from it as to be rejected out of hand.

But how does this give us the observed battle royale between chaotic good and lawful evil? Up there, it just looks like a circle. 

Okay, so we see the point (3, 3) in that circular band. How common is it for someone to be 3 standard deviations of lawful and 3 standard deviations of good? Not common at all. 3-sigma events are rare (about 1 in 740) so a person who was 3 deviations from the norm in both would be 1-in-548,000– a true rarity. Let’s multiply this “fringeness” function we’ve graphed by the (Gaussian) population density at each point.

That’s what the fringe, weighted by population density, looks like. There’s a lack of presence of people at positions like (3, 3) because there’s almost no one there. There’s a clear crescent “C” shape and it contains a disproportionate share of two kinds of people. It has a lot of lawful evil in the bottom right, and a lot of chaotic good in the top left, in addition to some neutral “swing players” who will tend to side (with unity in their group) with one or the other. How they swing tends to determine the moral character of an organization. If they side with the chaotic good, then they’ll create a company like Valve. If they side with lawful evil, you get the typical MacLeod process.

That’s the theoretical reason why organizations come down to an apocalyptic battle between chaotic good (Technocrats) and lawful evil (corrosive Sociopaths, in the MacLeod process). How does this usually play out? Well, we know what lawful evil does. It uses the credibility black market to gain power in the organization. How should chaotic good fight against this? It seems that convexity plays to our advantage, insofar as the MacLeod process can no longer be afforded. In the long term, the firm can only survive if people like us (chaotic good) win. How do we turn that into victory in the short term?

So what’s a Technocrat to do? And how can a company be built to prevent it from undergoing MacLeod corrosion? What’s missing in the self-executive and guild cultures that a 5th “new” type of culture might be able to fix? That’s where I intend to go next.

Take a break, breathe a little. I’ll be back in about a week to Solve It.