# Gervais / MacLeod 9: Convexity

Originally, I had intended the 9th part to be the one in which I solve this damn thing for good. Unfortunately, as that “9th post” crossed into 12 kiloword territory, I realized that I needed to break it up a bit. Even for me, that’s long. So I had to tear some stuff out and split this “final post” yet again. So here’s the 9th chapter in this ongoing lurid saga. (See: Part 1Part 2Part 3Part 4Part 5Part 6Part 7, Part 8). I am really trying to wrap this fucker up so I can get my life back, but I’m not going to solve it just yet… there are a few more core concepts I must address. Today’s topic, however, is convexity.

What is convexity?

Convexity pertains to the question: which is more important, the difference between excellent work and mediocre work, or that between mediocre work and noncompliance (zero)? For concave work, the latter is more important: just getting the job done is what matters, and qualitative concerns are minimal. For convex work, the difference between excellence and mediocrity is substantial and that between mediocrity and failure is small.

The theoretical basis for this is the logistic function, or “S-curve”, which is convex at its left side (looking exponential at y << 0.5) and concave on the right, as it approaches a horizontal asymptote (saturation point). Model input as a numerical variable i pertaining to resources, skill, talent, or effort. Then model task output as having a maximal yield Y, and the function Y * p(i) where p is a logistic function with range (0, 1) representing the proportion of maximum possible yield that is captured. Now, the inflection point (switch-over from convexity to concavity) is exactly where p(i) = 0.5. Taken in full, this logistic function is neither concave or convex. Yet, for most economic problems, the relevant band of the input range is narrow and is mostly on one side of the inflection point or the other. We can classify tasks as convex or concave based on what we know about the performance of the average.

To get concrete about it, consider exams in most schools. A failing student might be able to answer 60% of the questions right; an average one gets 80%, and the good ones get 90%. That’s a concave world. The questions are easy, and one needs to get almost all of them right to distinguish oneself. On the other hand, a math researcher would be thrilled to solve 50% of the (much harder) problems she confronts. With concave work, the success or completion rates tend to be high because the tasks are easy. With convex work, they’re low because the tasks are hard. What makes convex work worth doing is that, often, the potential yield is much higher. If the task is concave, it’s been commoditized, and it’ll be hard to make a profit by doing it. Even 100% completion will yield only marginal profits. If the work is convex, the most successful players can generate outsized returns. It may not be clear even what the upper limit (the definition of “100%”) is.

Convexity and management

Consider the following payoff structures for two tasks, A and B. A is concave; B is convex, and 0 to 4 represent degrees of success.

```| Performance | A Payoff | B Payoff | Q dist | R dist | ------------------------------------------------------- | 4 (Superb)  |      125 |      500 |  20.0% |   0.0% | | 3 (Good)    |      120 |      250 |  20.0% |  20.0% | | 2 (Fair)    |      100 |      100 |  20.0% |  60.0% | | 1 (Poor)    |       60 |       25 |  20.0% |  20.0% | | 0 (Awful)   |        0 |        0 |  20.0% |   0.0% | ------------------------------------------------------- ```

Further, let’s assume there are two management strategies, Q and R. Under Q, the workforce will be uniformly distributed among the five tiers of performance: 20% in each. Under R, 20% each of the workforce will fall into Good and Poor, 60% into the Fair tier, and none into the Superb or Awful tiers. R is variance-reducing managerial strategy. It brings people in toward the middle. The goal, here is to maximize bulk productivity, and we assume we have enough workers that we can use the expected payoff as a proxy for that.

For Job A, which is concave, management strategy Q produces an output-per-worker of 81, while R yields 96. The variance-reducing strategy, R, is the right one, yielding 15 points more. For example, bringing up the worst slackers (from 0 to 60) delivers more benefit than pulling down the top players (from 125 to 120).

For Job B, which is convex, strategy Q gives us an average yield of 165, while R delivers only 105– 60 points less. The variance-reducing strategy fails. We see more of a drop in pulling down the best people (from 500 to 250) than we gain in hauling the laggards (o to 25).

In short, when the work is concave, variance is your enemy and reducing it increases expected yield. When the work is convex, variance is your friend; more risk means more yield.

The above may seem disconnected from the problems of the MacLeod organization, but it’s not. MacLeod organizations are based on variance-reduction management strategies, which have worked overwhelmingly well over the past 200 years of concave, industrial-era labor. MacLeod Losers naturally desire familiarity, uniformity, and stability. They want variance to be reduced and will give up autonomy to have that. MacLeod Clueless (middle managers) take on the job of reducing variance in conditions for the Losers below them, and reducing volatility in performance for the Sociopaths above. Their job is to homogenize and control, and they do it well. It doesn’t require vision or strategy. MacLeod Sociopaths start out as the “heroic” risk-takers (entrepreneurs) but that caste often evolves (as especially as transplant executives come in) into a cushy rent-seeking class as the organization matures (necessitating the obfuscation enabled by the Clueless and the Effort Thermocline). The Sociopath category itself becomes risk-averse, out of each established individual’s desire to protect organizational position. The result is an organization that is very good at reducing variance and stifling individuality, but incapable of innovation. How do we come back from that?

In the concave world, the failures of the MacLeod organization were tolerable. Businesses didn’t need to generate new ideas. They needed to turn existing, semi-fresh ones into repeatable processes, and motivate large groups of people to carry out difficult but mostly simple functions. Variance-reduction was desired and encouraged. Only in the past few decades, with the industrial era fading and the technological one coming on, has there been a need for business to have in-house creative capacity.

Old-style organizations: the optimization model

The convex/concave discussion above assumes one dimension of input (pertaining to how good an individual is at a job) and one of output (observed productivity). In truth, a more accurate model of an organization’s performance would have a interconnected network of such “S-curve” functions for the relationships between various variables, many of which are hidden. There’d be a few input variables (“business variables”) and the things the company cares about (profit, reputation, organizational health) would be outputs, but most of the cause-and-effect relationships are hidden. Wages affect morale, which affects performance, which affects productivity, which affects the firm’s profits, which is its performance function. With all of the dimensions that could be considered, this function might be very convoluted, and while it is held to exist “platonically” it is not known in its entirety. The actual function relating controllable business variables to performance is illegible (due to hidden variables) and certainly not perfectly concave or convex.

So how does the firm find an optimal solution for a problem it faces?

This gets into an area of math called optimization, and I’m not going to be able to do it justice, so I’ll just address it in a hand-wavy way. First, imagine a two-dimensional space (if only because it’s hard to visualize more) where each point has an associated value, creating a 3-dimensional graph surface. We want to find the “highest point”. If that surface is globally concave, like an inverted bowl, that’s very easy, because there can only be one maximum. We can start from any point and “hill climb”: assess the local gradient, step in the most favorable dimension. We’ll end up at the highest point. However, the more convoluted our surface is, the harder the optimization problem. If we pick a bad starting point on a convoluted surface, we might end up somewhere sub-optimal. Thinking of it in topographical terms, a “hill climb” from most places won’t lead to the top of Mount Everest, but to the neighborhood’s highest hill. In other words, the “starting point” matters if the surface is convoluted.

Real optimization problems usually involve more than two dimensions. It is obviously not the case that organizations perform optimizations over all possible values of all possible business variables (of which there are an infinite number). Additionally, the performance function changes over time. As a metaphor, however, for the profit-maximizing industrial corporation, it’s surprisingly useful. One part of what it must do is pick a good “starting point” for the state of the business, a question of “What should this company be?” That requires non-local insight. Another part is the iterative process of refinement and hill-climbing once that initial point is selected.

This leads to the three-tier organization. People who are needed for commodity labor but not trusted, at all, to affect business variables are mere workers. Managers perform the iterative hill-climb and find the highest point in the neighborhood. In startup terms, they “iterate”. Executives, whose job is to choose starting points, also have the right to make non-local “jumps” in business state if needed. In startup terminology, they “pivot”.

The MacLeod organization gets along well with this computational model of the organization. MacLeod Losers are mediocre in dedication, but that’s fine. That aspect of them is treated as a hidden variable that can be modulated via compensation (carrots) or managerial attention (sticks). In the optimization model, they’re just infrastructure– human resources in the true sense of the word. The fault of MacLeod Clueless is that they aren’t strategic, but they don’t need to be. Since their job is just to climb a hill, they don’t need to worry about non-local “vision” concerns such as whether they’re climbing the right hill. That’s for someone else to worry about. They just assess the local gradient and move in the steepest upward direction. Finally, there are the MacLeod Sociopaths, whose goal is to be strategic and have non-local insight. Being successful at that usually requires a high quality of information, and people don’t get that stuff by following the rules. The source could be illegal (industrial espionage) or chaotic (experimental approaches to social interaction) or merely insubordinate (a programmer learning new technologies on “company time”) but it’s almost always transgressive. The MacLeod Sociopath’s ability to get information confers more benefit, in an executive position, than the negatives associated with that category.

Why the optimization model breaks down

In the model above, there is some finite and well-specified set of business variables. The real world is much more unruly. In truth, there are an infinite number of dimensions. Two things make this more tractable. The first is sparsity. Most dimensions don’t matter. For example, model “product concern” as a vector representing the products that a company might make (1.0 meaning “it’s the only thing we care about”, 0 meaning “not interested at all”). Assume there are 387 trillion possible conceivable products that a firm could create. That’s 387 trillion business variables; 386.99999…+ trillion of those entries are always going to be zero (excepting a major pivot) and can be thrown out of the analysis. Second is aggregation. For personnel, one could have a variable for each of the world’s 7.1 billion people (again, most being zero for ‘not working here’) but most companies just care about a few things, like how many people they employ and how much they cost. Headcount and budget are the important business variables. Whether John A. Smith, 35, of Flint, Michigan is employed at the company (i.e. one of those 7.1 billion personal variables) isn’t that relevant for most values of John A. Smith, so executives need not concern themselves with it.

Even still, modern companies have thousands to millions of business variables that matter to them. That’s more information than a single person can process. Then there is the matter of what variables might matter (unknown unknowns). If the optimization problem were simple, the company would only need one executive to call out starting points, but these information issues mandate a larger team. The computation that the organization exists to perform must be distributed. It can’t fit on one “node” (i.e. one person’s head). That also mandates that this massively high-dimensional optimization problem be broken down as well. (I’m ignoring the reality, which is that most people in business don’t “compute” at all, and that many decisions are made on hunches rather than data.)

As far as many dimensions are separable (that is, they aren’t expected to interact, so the best values for each can be found in separation) the problem can be decomposed by splitting it into subproblems and solving each in isolation. Executives take the most important business variables where it is most likely that non-local jumps will be needed, such as whether to lay off 15% of the workforce. The less important ones (like whether to fire John A. Smith) are tackled by managers. Workers don’t participate in the problem-solving; they’re just machines.

This evolves from an optimization model where business variables and performance functions are presumed to exist platonically, to a distributed agent-based model operated by local problem-solving agents. This is a more accurate model of what actually happens in the corporation, made further amusing by the fact that the agents often have diverging personal objective functions. Centralized computation is no longer the most important force in the company; it’s communication between the nodes (people).

Here’s where MacLeod comes in to the agent-based model. MacLeod Losers consume information and turn it into work. That’s all they’re expected to do, and ideally the only thing that should be coming back is one word: done. MacLeod Clueless furnish information up and down the food chain, non-editorially because they aren’t strategic enough to turn it into power. They tell the Losers what to do, and the Sociopaths what was done, and they aren’t much of a filter. The only information they take in, in general, is what information others want from them. The MacLeod Sociopaths are strategic givers and takers of information, and (having their own agendas) they are selective in what they transmit. Organizations actually need such people in order to protect the top decision-makers from “information overload”. It’s largely the bottom-tier Sociopaths who participate in dimensionality reduction and aggregation, so they’re absolutely vital; however, they make sure to use whatever editorial sway they have toward their own benefit.

Optimization and convexity

The actual performance function of a company, in terms of its business variables, is quite convoluted. It’s generally concave in a neighborhood (enabling managers to find the “local hill”) but its global structure is not, necessitating the non-local jumping afforded to executives. The underlying structure, as I said earlier, is driven by an inordinate number of hidden variables. It might be best thought of as a neural network of S-curve functions (“perceptrons”) wherein there are elements of concavity and convexity, often interacting in strange ways. It’s not possible for anyone to ascertain what a specific organization’s underlying network looks like exactly. The overall relationship between business variables (inputs) and performance (output) is not going to be purely concave or convex. The best one can hope for is a well-chosen initial point in which the neighborhood is concave.

For typical organizations and most people in them, concavity has a lot of nice properties. For one thing, it tends toward fairness. Allocation of resources to varying parties, if the input/output relationships are concave, is likely to favor an “interior” solution– everyone gets some– because marginal returns diminish as the resource is allocated to richer people. If the input/output relationship is convex, resource allocation can favor an “edge” solution where one party gets everything and the rest get none, which tends to exacerbate the “power law” distributions associated with social inequalities: a few (who seem the most capable of turning those resources into value) get much, most get little or nothing. Another benefit of concavity is that performance relative to a standard can be measured. At concave work, the maximum sustainable output is typically well-studied and known, and acceptable error rates can be defined. With convex work, no one knows what’s possible. Once a maximum is established and can be reliably attained, the task is likely to become concave (as people develop the skills to perform it successfully more than 50% of the time). Research is inherently convex: most things explored don’t pan out, but those that do deliver major benefit. When those explorations lead to repeatable processes that can be carried out by people of average motivation and talent, that’s concave, commodity work.

MacLeod organizations exist as a risk trade between those who want to be protected from the vagaries of the market, so creating islands of concavity and easiness is kind of what they do. The Big World Out There is a place with many pockets of convexity, plenty of bad local maxima, and a difficult and mostly unexplored landscape. The MacLeod organization provides its lower-level workers with access to an already-explored and safe concave hill neighborhood. Executives read maps and find start points. Managers just follow the steepest gradient up to the top, and workers just have to follow and carry the manager’s stuff.

Technology and change

There’s a problem with concave work. It tends to be commoditized, making it hard to get substantial profits on it. If “100% performance” can truly be defined and specified, then it can also be achieved mechanically. Not only are the margins low, but machines are just better at it than humans: they’re cheaper, don’t need breaks, and don’t make as many mistakes. Robots are taking over the concave work, leaving us with convexity.

We cannot compete with robots on concave work. We’ll need to let them have it.

Software engineering is notoriously convex. First of all, excellent software engineers are 5 to 100 times more productive than average ones, a phenomenon known as the “10X engineer”. As is typical of convex projects, most software projects fail– probably over 80 percent deliver negative net value– but the payoffs of the successes are massive. This is even more the case in the VC-funded startup ecosystem, where companies that seem to show potential for runaway success are sped along, the laggards being shot and killed. In a convex world, that’s how you allocate resources and attention: focus on the winners, starve the losers.

Convexity actually makes for a very frustrating ecosystem. While convex work is a lot more “fun” because of the upside potential and the challenge, it’s not a great way to make a living. Most software engineers get to age 30 with no successful projects (most of this being not their fault) under their belt, no credibility on account of that series of ill-fated projects, and mediocre financial results (even if they had successes). Managing convex work, and compensating it fairly, are not things that we have a society have figured out how to do. For the past 200 years– the industrial era, as opposed to the technological one that is coming on– we haven’t needed to do it. Almost all human labor was concave. What little convex work existed was generally oriented toward standardizing processes so as to make 99.9% of the organization’s labor pool concave. We are now moving toward an economy where enormous amounts of work are done by machines, practically for free, leaving only convex, creatively taxing work.

The fate of the MacLeod organization

MacLeod organizations, over the past 200 years, could perform well. They weren’t great at innovation; they didn’t need it. They got the job done. One of the virtues of the corporation was its ability to function as a machine made out of people. It would render services and products not only at more scale, but much more reliably, than individual people could do. The industrial corporation co-evolved with the failings of each tier of the MacLeod organization, hence converging on the “optimization model” above that uses the traits of each to its benefit. Of course, I do not mean to suggest that these “computations” are performed in reality, but the metaphor works quite well.

The modern technological economy has created problems for that style of organization, however. Microeconomic models tend to focus on a small number of business variables– price points, quantity produced, wages. Current-day challenges require thousands, often ill-defined. What product is one going to build? What kind of people should be hired? What kind of culture should the company strive toward, and how will it enforce those ideals? Those things matter a lot more in the technological economy. Hidden variables that could once be ignored are now crucial, and industrial-era management is falling flat. Combine this with the convexity of input/output relationships regarding individual talent, effort, and motivation, and we have a dramatically different world.

The islands of concavity that MacLeod organizations can create for their Losers and Clueless are getting smaller by the year. The ability to protect the risk-averse from the Big World Out There is diminishing. MacLeod Sociopaths were never especially scrupulous about keeping that implicit promise, but now they can’t.

Even individuals now have to make non-local (formerly executive-level) decisions. For a concrete example, consider education. The generalist education implicitly assumes that most people will have a concave relationship between their amount of knowledge in an area and utility they get from it. It’s vitally important, as most educational institutions see it, for one to first get a mediocre amount of knowledge about a lot of subjects. (I agree, for non-economic reasons. How can a person know what is interesting without having a wide survey of knowledge? A mediocre knowledge gives you enough to determine if you want to know more; with no knowledge, you have no clue.) However, there’s no such thing as a Generalist job description. The market doesn’t reward a breadth of mediocre knowledge. People need to specialize. In 1950, having a college degree bought a person credibility as someone capable of learning quickly, thus entry into the managerial, professional, or academic ranks. (Specialization could begin on the job.) By 1985, one needed a marketable major: preferably, math, CS, or a physical science. In 2013, what classes a person took (compilers? machine learning?) is highly relevant. The convex valuation of a knowledge base makes deep knowledge in one area more valuable than a broader, shallower knowledge. Choosing and changing specialties is also a non-local process. A well-rounded generalist can move about the interior by gradually shifting attention. The changing specialist must jump from one “pointy” position to another– and hope it’s a good place to be.

In technology especially, we’re seeing an explosion of dimensionality. General competence doesn’t cut it anymore. Firms aren’t willing to hire “overall good” people who might take 6 months to learn their technology stacks, and the most credible job candidates don’t want to pin their careers on companies that don’t strongly correspond with their (sometimes idiosyncratic) preferences. When there’s a bilateral matching problem (e.g. dating) it usually has something to do with dimensionality. Both sides of the market are “purple squirrel hunting”.

This proliferation of dimensionality isn’t sustainable, of course. One thing I’ve come to believe is that it has an onerous effect on real estate prices. That might seem bizarre, but the “star cities” are the only places that tolerate purple squirrel hunting. If you’re a startup that wants a Python/Scala/C++ expert with production experience in 4 NoSQL products and two PhDs, you can find her in the Bay Area. For some price, she’s out there. That’s not because the people in the Bay Area are better; it’s that, with more of them, you get a continuous (it’s available at some price) rather than discrete (you might wait intolerably long and not find it) market for talent– and also for jobs, from a candidate’s perspective– even if you’re trying to fill some ridiculous purple squirrel specification. That’s what makes “tech hubs” (e.g. Bay Area, New York, Boston) so attractive– to candidates and companies both– and a major part of what keeps them so expensive. The continuous markets make high-risk business and job-hopping careers– that aren’t viable in smaller cities unless one wants to move or tolerate remote work– possible. Since real estate in these areas is reaching the point of being unaffordable for technology workers, I think it’s a fair call to say that this dimensionality explosion in technology won’t continue forever. However, convexity and high dimensionality in general are here to stay, and about to become the norm for the greater economy. The convexity introduced by an economic arrangement where an increasing bulk of commodity labor is dumped directly on machines has incredible upsides, and is very attractive. Now, in the late-industrial era, global economic growth is about 4-5 percent per year. In the thick of the technological era– a few decades from now– it could be over 10% per year.

If MacLeod rank cultures are going to become obsolete, what will replace them? That I do not know for sure, but I have some thoughts. The “optimization model” paints a world where the relevant business variables are known. Executives call out initial values (based on non-local knowledge) for a gradient ascent performed by managers. As the business world becomes high-dimensional– too many dimensions for any one person to handle them all– it begins to break down the problem and distribute the “computation” (again, solely in metaphor). High-ranking executives handle important dimensions (sub-problems) where tricky non-local jumps might be in order. Managers handle less-important ones where continuous modulation will do. Getting the communication topology right is tricky. Often the conceptual hierarchy that is created will look suspiciously like the organizational hierarchy (Conway’s Law?) This leads to an interesting question: is this hierarchy of people– which will limit the firm’s capacity to form proper conceptual hierarchies and solve its own problems– even necessary? Or is it better to have all eyes open on non-local, “visionary” questions? Is that a good idea? Organizations claim to want their employees to “act like owners”. Is that really true? With the immense complexity of the technological economy, and the increasing inability of centralized management to tackle convexity (one cannot force creative excellence or innovation by managerial fiat) it might have to be true.

Enter the self-executive. Self-executive people don’t think of themselves as subordinate employees, but as free agents. They don’t want to be told what to do. They want to excel. A manager who will guide one (mentorship) gets loyalty. However, typical exploitative managers get ignored, sabotaged, or humiliated. Self-executive employees are the ones who can handle convexity, and enjoy the risk and challenge of hard problems. They strongly toward chaos on the civil alignment spectrum. These are the people one will need in order to navigate a convex technological economy, and the self-executive culture is the one that will unleash their capabilities.

That said, the guild culture has a lot to add as well and should not be ignored. There’s a lot of lost work in exploration that can be eliminated by advice from a wise mentor (although if things change, as they do more rapidly these days, that “don’t go there” advice might sometimes be best discarded). The valuation of knowledge and skill are so strongly convex that there’s immense value generation in teaching. Not only should that not be ignored, but it’s going to become a critical component of the working culture. Companies that want loyalty are going to have to start teaching people again. Self-executives don’t work hard unless they believe they’re learning more on the work given to them than they would on their own– and these people tend to be fiercely autodidactic.

This brings us to the old quip. A VP tells his CEO that the company should invest more in its people, and he says, “What if we spend all that money training them and they leave?” The VP’s response: “what happens if we don’t and they stay?” That ends up looking like MacLeod rank culture over time. There’s a lot to be learned from guild culture, and when I finally Solve This Fucking Thing (Part 11? 12? 5764+23i?) I won’t be able to afford to overlook it.

## 29 thoughts on “Gervais / MacLeod 9: Convexity”

1. schwolop |

I think you’ve just motivated me to finally get around to writing the essay I’d planning applying my PhD to working life. The PhD solved an optimisation problem where the state space kept changing unpredictably, and the cost-function included the time taken to compute the optimisation. Essentially a mathematical formulation of “sometimes it’s better to just do something in a roundabout way rather than spending a long time thinking about the better way only to discover it wasn’t much better after all.” The final result essentially proved that on average, using some percentage of your available time to think about the possibility of better options whilst executing the best option found so far, is the right approach. But the best possible approach is if you can measure some aspects of your method and dynamically adjust that percentage as the situation changes.)

I’ve always felt this applies to decision making in general, but especially so in terms of the workplace. In your analogy of executives choosing when to switch hills, this technique tells them how often to do so, and how much of their time to spend thinking about it.

I also wrote up an analysis of myself and my workplaces in terms of your treatise, mainly as an excuse to start writing more again: http://www.drtomallen.com/1/post/2013/03/if-michael-ochurch-is-wrong-then-im-probably-going-to-kill-you.html

• angelbob |

Huh. That suggests (to steal from a completely different domain) that in Multi-Armed Bandit A/B-testing-style problems in a web site, you could probably get significantly better performance by dynamically adjusting your “learning percentage” as well — trivially by reducing asymptotically over time, or potentially doing it according to the results you got.

That’s starting to hurt my head, which is probably a good sign.

There’s probably a really, really good method to be mined from that.

Thanks!

• schwolop |

Reducing the ‘learning percentage’ is generally called the “epsilon-greedy strategy”, and works well in many cases. It’s a trade-off between learning and exploiting that decreases learning over time. When the arms change their values/costs with time, then you need to increase the learning rate again when you think the underlying distributions might have changed sufficiently to justify it.

For websites, I expect people’s behaviours given different settings/colours/layouts/etc won’t change fast enough to justify this kind of effort. You really only do A/B testing in the first place because you expect that after having learnt the optimum settings, you can just leave things alone thereafter. If you had to continually change things dynamically the web would be a very weird place!

2. Portlander |

Minor nit, in case you plan to make this series into an e-book and launch a career in consultancy… ;^)

For the past 200 years– the industrial era, as opposed to the technological one that is coming on– we haven’t needed to do it. Almost all human labor was concave…

Obviously, concave work pre-dates the 200 years of Industrial Revolution. I’d say it’s about as old as life itself. Maybe viruses have made the leap to a convex model of existence? :-)

Anyway, I do agree as you state right after that excerpt, that what made the Industrial Revolution unique was that it allowed/made possible (not sure what the right verb would be) a few convex geniuses to build tools that leveraged otherwise concave workers to be 10 to 1000 times more productive. In that regard, the industrial revolution is the history of moving from a concave to a convex existence.

Conversely, the tech revolution is unique in that a few convex geniuses make the tools themselves so productive that concave workers are not even required. In that regard, concave workers’ productivity is returning to 0. Perhaps, the history of the tech revolution is about moving from a convex existence with a positive derivative to one with a negative? We’re still increasing output/unit work, but the rate of change is slowing down.

In this new existence, I believe the social contract is paramount to get right. We can easily reach a localized maximum (in fact, are at one now) with the 0-1% doing incredibly well, the 1-10% doing well, the 10-20% doing OK, and everyone below that seeing a decrease in their real standard of living, mitigated by the tech itself so that the 20-100%’s relative standard of living feels the same or even better if consumer electronics and cheap, processed food are your preferred fruits. The economists call this an hedonic adjustment :(.

• asdf |

Yes, what gets me about all of this singularity talk is that it just isn’t showing up in the data. Growth in the first world is practically nil. The third world is mostly catching up to already existing technology after shaking off communism. We aren’t really seeing any growth outside of catch up. It is interesting to see people talk about the singularity coming in one sentence and then remark how they can’t even afford to start a family in the next.

If your existance is consumer electronics & cheap processed food then maybe you can get more of that now and even in the future. However, anything that requires specialized labor (like medical care) or scarce natural resources (like energy) isn’t more available then it used to be. I find it strange to be talking about exponential growth at a time when living standards are shrinking for the median person in the developed world.

Lastly, if things get better it will be only because the plebs are being allowed to buy massed produced crap on their EBTs. Nearly all the rest of society will be closed to them. The convex society will be a society that only the top X% can participate in because there is genetic threshold to convex work.

• You’re wrong about energy — it turns out that over any 20-year period that you choose to bet on, energy prices have come *down*, not gone up, in inflation-adjusted dollars. Maybe the next 20 years won’t be the same, but historically speaking, that’s not the way to bet. The junk you’re talking about uses more energy, and we have more energy available than before to use it. “One average person’s worth of energy for a year” is more expensive, but only because we use more, not because it’s more expensive per watt or per gallon of gasoline.

You’re right about medical care, of course.

And that’s a very good point about convex work. In general, it’s hard to imagine how more than 10-20% of the population could be doing convex work at any given time. Even assuming high churn (convex work is often *hard* and careers might be short), it still seems ludicrously optimistic to assume 70% of people could do much convex work in their career, and 30% unemployment is frighteningly high by historical standards.

You’re not the first to notice that convex work seems inherently un-populist and un-democratic, obviously. But yeah, what do we do with everybody else? I think Michael’s occasional idea of a Basic Income (i.e. guaranteed minimum income that buys only the basics) will wind up with exactly what you’re worried about. Experiments with Basic Incomes show that they seem to be massively demotivating for teenagers just starting work (work as a teenager is hard, and not yet very rewarding), which you’d expect to have a nasty domino effect on overall productivity.

So yeah, maybe we wind up with a two-level society — work, mostly convex work, for a minority of people with a carefully-inculcated work ethic, and idleness for a majority of people who don’t see any practical way out.

(“Working *is* practical.” “Yes, but almost-all-convex work, very hard and specialized work, will also tend to require a lot of experience or a lot of training. Even a corporate Guild Culture sucks at giving a chance to people without a lot of previous opportunities. Think about getting started in investment banking as an inner-city kid with a thick ghetto accent but brains and a good work ethic. It sucks. And future convex work looks like it will suck a lot more to get started.”)

• asdf |

“Maybe the next 20 years won’t be the same, but historically speaking, that’s not the way to bet.”

Why?

We found some stuff that has takes millions of years to make and burned it up. That made for a pretty sweet century. Since it takes millions of years to make presumably we will reach some point where the returns get diminishing and maybe negative. I don’t know if we’ve been there, will be there, or if its a long ways off. By definition its coming at some point though.

For most of my adult life rising energy prices have been the norm. And with a couple billion Asians industrializing I expect there to be pretty strong demand for some time. We may yet be bailed out by some as yet unknown technology. Then again we might not.

I think you can expand the pool of useful labor if you’ve got the right culture. There is plenty of useful but not difficult work that requires the right attitude rather then high IQ. For instance there are tons of service jobs that I’d have a polite young Japanese youth do for me that I wouldn’t let some tattoed American punk with attitude do. However, that’s a whole other topic and I’m sure there is still a limit to this effect.

• > Why?

Because we’ve had exactly that reasoning for 100+ years, and so far it’s never panned out. Maybe this coming 20 years will be different. Presumably *some* 20 years will be different. I say “historically speaking that’s not the way to bet” because at no point in American history could you have bet against it in a 20-year period and won. As for “some unknown technology” — solar won’t have far to go, given how far it’s gone recently and given how much it gains by scooping up outdated computer technology. Solar is my bet.

As for expanding the pool of labor — yes, as you say, for things like service jobs. Expanding the pool of labor beyond 70% (which is crazy-high historically) for *convex* labor will be hard.

Maybe we’ll go to a most-service economy. Stranger things have happened. But then you’re going to have the division between convex-labor elite and service-job plebs that you’re talking about.

• asdf |

Given the nature of the product (fossil fuels mostly) it just seems like a 100 year track record isn’t particularly good enough. If you know:

1) That by definition there will be a peak and then a decline “at some point”

and

2) There is some evidence (insert factors XYZ) that may be coming “soon”

That seems good enough that citing the past 100 years isn’t a totally overiding piece of evidence against.

• It isn’t a totally overriding piece of evidence. You’ll notice that I didn’t say “no bet you could place on this at any point in the future will fail.”

I said that, “historically speaking, that’s not the way to bet.”

Historically, the way to bet is that all commodity prices go *down* over 20+-year periods.

That’s not because nothing else could ever happen.

But fundamentally, while specific prices can go up (whale oil!), in general energy prices have always gone *down*.

Maybe fossil fuels will define the last major energy source, as whale oil didn’t and like the horse wasn’t the last word in transportation.

But if I had to put down money for this coming 20 years, starting today, I would bet on energy prices being *lower* at the beginning of 2033 than they are today. After inflation, anyway.

• Asdf, you don’t have the slightest idea whether it’s generic or not, and neither does anyone else, so it’s disingenuous (to say the least) to talk as if you did.

• asdf |

Slightest idea that what is genetic? The bell curve?

If that is the thing being questioned I’m just going to declare victory in the debate on weight of obvious evidence.

• Intelligence is actually pretty strongly genetically linked. Earning potential is, if anything, even *more* clearly genetically linked. Yes, even factoring out who you’re raised by.

So the idea that capability at convex labor is genetically linked is at least not a large stretch. Not quite a slam-dunk, but the most obvious hypothesis.

• Juan |

Have you got a citation for any of your claims? I know about the studies linking IQ and genetics; the correlation (and it’s just a correlation, working out the causality is too complex atm) is usually around 0:38-0:48. So I’ll give you that one. But the second claim about earning potential has me slightly puzzled. Where’s the evidence for that one?

Incidentally, anyone interested in the issue of intelligence and heritability ought to read these blog posts from a working statiscian:

http://vserver1.cscs.lsa.umich.edu/~crshalizi/weblog/520.html

http://vserver1.cscs.lsa.umich.edu/~crshalizi/weblog/523.html

@asdf Are you referring to The Bell Curve book? If so, and you hold it to be a piece of “obvious evidence”, then you’re in very shaky ground indeed. If not, apologies. ;-)

• asdf |

I’m talking about the very concept of a “bell curve” in human traits. IQ, hieght, etc. Differing traits and abilities should be a very obvious concept to people. And the idea that evolution or genetics “stops at the neck” is just silly.

Steve Hsu has done a lot of good work on showing the importance of ‘g’ on life success. I recommend a persual of his website:

http://infoproc.blogspot.com/

The goal of intelligence research isn’t to prove that heritability = 1, or that one measure of human ability is the only thing that determines “success” in life. One need only proof that such factors are “significant”. If so they should account for how we view the world, and perhaps shape how we view public policy.

I’m personally of the opinion that blank slatism (assumed equality of ability) is a totally malicious concept. Its purpose is to let people with great genetic gifts (of which ‘g’ is just one) claim everything they have in life is because their “character” and that those that fail deserve it because of their “character”. It’s a disgusting idea that has poisoned our culture and our public policy.

“Do I really believe that the heritability of IQ is zero? Well, I hope by this point I’ve persuaded you that’s not a well-posed question. What I hope you really want to ask is something like: Do I think there are currently any genetic variations which, holding environment fixed to within some reasonable norms for prosperous, democratic, industrial or post-industrial societies, would tend to lead to differences in IQ? There my answer is “yes, of course”.”

So the author clearly thinks that it passes the significance test, all protests about the exact numbers despite.

“on the distinction between “human equality” and “genetic identity”, and ask why it is so important to you that IQ be heritable and unchangeable. ”

It’s importance should be really obvious. The damage done by the blank slate approach is quite clear.

I once worked in one of those Asian cram schools trying to boost SAT scores. We tracked the data, generally speaking we couldn’t budge it much at all and it was subject to massive diminishing returns. We took the money just the same.

The thing I remember most from my time there was this retarded kid that was there. He studied every single night after school and eight hours each day on the weekend. His whole childhood was studying. He had a Harvard cap on. His parents insisted he would go to Harvard. Everyone can. YOU JUST HAVE TO WORK HARDER. It was as hardcore Asian as you can get.

The kid was clinically retarded. He would be lucky to get 1000/1600 on the SAT. They wanted him to score in the top 1%. They yelled at him and abused him. It was terrible to watch. I screamed at my manager to stop it and walked out when she said that the deal was “too lucrative”.

The same could apply to smart kids. I had a Korean kid in my school that got a 1550/1600. He had studied a lot. His parents beat him for not getting a perfect score. In their minds there was no such thing as naturally only being “so smart”. Anyone can do anything if they just work harder. Any failure is the result of bad character and not some lack of natural ability.

Blank slatism is a dangerous and vile idea that has caused enough damage already. Its very obvious that genes play a huge role in our life outcomes. People who deny this do so for selfish reasons and its hurt enough people already.

• asdf |

Going futher on the genetic ability issue.

My Dad worked for a union for many years. Hard labor. Until recentely it paid a living wage. He took his work very seriously, in fact an aura of pride came through in any kind of work he did. Growing up, before he got sick, there was a line of perfect attendance and employee of the month awards along our starwell.

The union recentely got busted. They finally had enough illegals to break the picket line. They had to take a 40% paycut and lost their medical benefits. This wasn’t a case like the autos where they were losing money. The company was making a profit at those wage rates. They just wanted more.

The same thing played out all around America the last several decades. Men of pretty average IQ really got the shaft. The only answer our elites could come up with is “go to college”. Even if these guys were 18 again, most of them don’t have the IQ to handle college material. They aren’t retarded, but I’ve met many of them and they are about smack dab in the middle of the curve. My Dad was probably the smarter among them (graduated from a fourth tier college on a wrestling scholarship).

People who had the IQ to go to college then start acting like their success is somehow all about their “character”. Obviously, it has nothing to do with changing economic trends that have made ‘g’ more important. No, its all about them. And when “bitter clingers” like my Dad get their wages smashed they act like all of a sudden he lost 40% of his “character”. If he’s struggling, he deserves it.

In moments of empathy they will say something like, “well, maybe its because they need more education. Maybe their environment is bad.” And perhaps those things help sometimes. However, if it’s a ‘g’ problem, there are no jobs for people with a certain ‘g’ level, and you can’t change ‘g’ all that much, then we are really talking about a non-solution. The solution to send everyone to college meant all the surplus people that shouldn’t be there graduated without jobs and a bunch of debt.

If we could just talk about how not everyone can be everything then maybe we could come up with solutions. Maybe we could have industrial and education policy built around it (like Germany and Japan). Maybe people who win the genetic lottery could realize those that lost it don’t have “bad character” and maybe they shouldn’t take so much pride in things they were given at birth.

The implications of this stuff are so far reaching, and the failure of nearly all of our policies to improve the lives of huge swaths of the population, that it has to be addressed.

• I try to stay away from IQ/g debates because I find them goddamn depressing, but this is a very good post.

It wasn’t until my mid-20s that I realized how much I had internalized this ridiculous, and self-serving, correlation of natural intelligence with moral superiority. What happened? I met _plenty_ of high-IQ people with degenerate character. Enough not to call them flukes or attribute it to garden-variety high-IQ social ineptitude.

3. Juan |

Hey Michael,

This song is a textual case of an executive (sociopath/technocratic) point of view. Or at the very least an strategic one.

Thought it would make you smile.