Why don’t we treat geniuses like professional athletes?
I mean in the work-environment sense, rather than the celebrity-and-endorsement-deals sense.
A professional sports team gets a lot of benefits that are designed to keep physically talented people performing at their peak. They have support for things like recovery, sleep, nutrition, physical fitness, and of course the specific skills they use to play the game. This support takes the form of specialized personnel who are also employed by the team, whose job it is to work with the athletes for those purposes. The whole environment is geared towards performance maintenance.
The basics of sleep, diet and exercise are required for optimal performance across all domains; being in an environment that optimizes for them is an advantage.
Sports teams balance practice and games. Practice still makes sense for knowledge work; games analogize to projects/development/etc.
Decomposing practices is where the interesting bits might be. In athletics, there is almost always a ready-made set of drills for any particular skillset, which can be prioritized by a coach working with an athlete. There is already a notion of managing the workload: athletes have overtraining and thinkers have burnout.
In The Power of the Context Alan Kay describes funding people over projects and orienting them with a vision in lieu of specific goals. This sounds suspiciously like persuading geniuses to act as though they were on a team. Alan makes the comparison himself:
“Our game is more like art and sports than accounting, in that high percentages of failure are quite OK as long as enough larger processes succeed.”
I am tempted to go further and say that in the context of things like science or math research failures are still a positive contribution insofar as they establish that something doesn’t work, which makes future attempts more likely. This isn’t much of a thing in sports, which are entirely built around repeatable object-level activities; striking out does not make the next batter more likely to hit.
It seems like the object vs meta level distinction also highlights where the analogy breaks down. In a game like football, each player has a specialized role which contributes to moving the ball down the field. They can spend time mastering a set of moves which they adapt on the fly and can be relied on consistently. There is not a clear place for that in service of the PARC vision of “interactive computing as a complementary intellectual partner for people pervasively networked world-wide”.
We could torture the analogy ruthlessly and say that visions are meta-goals and that a meta-athlete could practice their meta-skills of finding how to complement the team’s work in advancing the vision. That even sounds sort of plausible, until you come up against the question of how to define those meta-skills. I feel like a checklist that goes like “Can you solve a simplified version of the problem? Can you generalize a solution from a similar problem?” doesn’t seem to cut it. Though such a checklist would hardly be a bad idea. Based on The Rocket Alignment Problem I envision motivational posters hanging around that go “The beacons are lit! Gondor calls for A.I.D: Articulate the confusion; Isolate the confusion; Dissolve the confusion.” Also, I am reminded of a talk given by Gian-Carlo Rota called Ten Lessons I Wish I Had Been Taught, in which he makes note of the fact that Erdos and Hilbert both employed a few tricks consistently in most of their work. Perhaps the role of the head meta-coach is to choose people such that their tricks complement each other well.
So we have the problem of identifying what skills exactly should be the focus of training and practice. We also have the problem of a shortage of personnel to serve as trainers and coaches, though I wonder if this is as intractable as it seems at first blush. The lion’s share of training is not so much mastery of the skill yourself as having an outside-and-informed perspective on someone else’s execution. The question is, how much is enough? A second question is, could this be automated instead?
Just to add to the analogies I drew in “The Power of the Context”. In baseball, an “error” (or “failiure”) is not catching a catchable flyball, etc., But, striking out is not an “error” or “failure”—it is better considered as “the overhead for sometimes accomplishing something really difficult”.
To bring this analogy to Xerox Parc, the technical aspects of computing—such as building a physical computer or an operating system or new programming language—should be successful “98%” or more of the time (this is roughly the fielding percentage in baseball). These require technical skills for which quite a bit of knowledge and practice has to be acquired and done in advance. It is a kind of engineering, often with some new design elements, but where “the bridge has to stay up”.
The really difficult parts of computing lie in attempts at inventing ways to create enormous new leverages via new kinds of organizations and designs. Here we should be ecstatically happy if we achieve the .406 that Ted Williams reached in 1941. The 60% this doesn’t work out is just “overhead”.
The downside of skill and knowledge is the temptation to use memory before thinking things through. The downside of ignorance and cleverness is that usually worse than “reinventing the flat tire” (which is all too common these days).
I’ve advocated “learning everything” and then “forgetting it except for the perfume”. In other words, though “most ideas are mediocre down to bad”, one has to have them freely rather than just applying technique.
The abundance of bad ideas can interfere, so you have to get rid of them somewhere. A good idea will have something like an odor to it that will allow one to find relevant knowledge in the past (often a very different past than the one that led to the present). This knowledge will help vet the idea, and eventually allow the weakest part of the process—one’s cleverness—to possibly do something worthwhile for once.
Honored to hear from you!
The intuition I have is that in a research context striking out isn’t just overhead but a positive contribution; all the other people working on the problem can now see that is not the answer. We can also look at why it wasn’t the answer, which is a source of new information. Therefore the next guy is more likely to get a hit.
It seems like everyone treats this kind of thing as trivial—it’s why we have scientific journals after all—but what I don’t see is much articulation of what value comes from where, and how to keep it. It looks to me like Xerox PARC did an amazing job of capitalizing on all of the information valuable to progress, and I suspect that’s because it was captured in the environment.
As a specific example, you have mentioned elsewhere that peer review didn’t make sense for PARC. Clearly eliminating the bureaucracy was a factor, but I suspect it is more important that what was happening instead did a superior job of delivering the same value that peer review is meant to. The team-of-peers has knowledge of the environment, familiarity with the previous work, contact with the generative process for an idea, and they can provide a new perspective on most any element of each other’s work at any time. Regular peer review is a static and passive check of correctness; because the PARC example was active and dynamic I want to call it “peer stabilization”.
I guess what I am gesturing at is the group is the unit of action. I suspect that if we want to do great things, or even just good things consistently, we need to build the context for the group. Then if it is made up of amazing people it will do amazing things.
Maybe we can disentangle the context from the vision, or the how from the why. Then we could move building powerful contexts into technical execution territory, waiting only for an appropriate vision or need to motivate them. I bet if I could break all this down into “value-added” language businesses and governments would be more willing to give it a shot.
Another problem is the problem of goals. Athletes have a very clearly defined, concrete goal. Run fast. Jump high. Score points. Genius, on the other hand, seems to lie in being able to redefine goals, or at least modify goals to make them more attainable.
My intuition (and it really is nothing more than an intuition) is that we don’t (and shouldn’t) treat geniuses like athletes because genius and athletics are on opposite sides of the explore/exploit dichotomy. Genius is all about exploring a problem space, and finding new solutions (and maybe even new problems). Athletics is about executing a set of strategies with maximal efficiency to reach a goal by a known route, as quickly and with as little expenditure of energy as possible.
The dichotomy isn’t hard-and-fast. The best athletes will be able to come up with new tactics and use those to win games more efficiently. The best geniuses will be able to execute fairly competently and efficiently upon their ideas. But in terms of emphasis, I think genius is much more about exploration, with exploitation (or execution) being an afterthought. Athletics, on the other hand, is all about exploitation (or execution). While there may be some exploration, that exploration is necessarily constrained by the (fixed) rules of the game.
For this reason, I think applying athletics tactics or even athletics metaphors to genius is misguided.
I’d add that genius isn’t limited to producing value adding work for only a couple of hours a week. Nor are there a set number of geniuses which a company is allowed to employ. That doesn’t mean there aren’t any lessons to learn but the dissimilarities mean the lessons won’t be straightforward.
The idea of having supporters for geniuses seems good. But drills sound really boring, and I don’t even think they would help. As a math researcher, I already get practice from solving problems that seem interesting, and doing specified drills would be throwing away the information about what I find interesting in favor of some other heuristic, which is unlikely to track cognitive usefulness as well as curiosity does. In the words of Isaac Newton:
Additionally, many intellectual fields (math, programming, science) largely consist of factoring out repeated patterns into named constructs, so repeating the same thing over and over again is often an antipattern. Some techniques are repeated, but most of the difficulty is in finding the right abstractions rather than in repeatedly applying them.
(There are some cases where drills help, such as in memorizing multiplication tables, but they are rarer than cases where they are counterproductive)
Where athletic coaches have drills ready, for research I feel like it would be more like a procedure for identifying and rectifying a mistake. I strongly suspect that this falls under the heading of “things good researchers do anyway”, for example:
1. When checking a conclusion, notice that one element of the arguments is too weak
2. That element is too weak because it lies outside the researcher’s core of expertise and so the implications were unclear to them
3. The researcher seeks out a colleague who has better expertise so as to understand the implications better
The thing is I expect a very large difference between this being something a researcher may or may not do on their own, versus something that will happen because it is the group expectation and everything is organized to make it as easy as possible. The more reliable this kind of supporting infrastructure is, the more we could extend it down below the level of genius (to turn mediocre researchers into competent ones, say).
Strongly agree that research progress usually happens because of networks of individuals with good norms/protocols such as checking each other’s work.
The things you mention seem more like skills to be taught once (or a few times) and practiced naturally, than things to be drilled repeatedly.
This has been my experience at tech companies; those perks are there for a reason. There has been relatively little in the way of ‘sleep coaching’ or similar things, but I definitely had access to a nutritionist, and the free lunches were somewhat optimized along this dimension, and so on.
I had this thought too. It seems like there is a significant difference between ‘these resources are here if you want them’ and ‘doing these things is a part of your job’, though. Even just switching from opt-in to opt-out is a pretty big impact in lots of contexts, and I expect strategic pressure to participate to be more impactful still.
To return to the question that inspired this post, I think most of the reason is that it’s often less clear how to use genius to some specific end. In most lines of business geniuses often don’t work well because teamwork-related factors dominate performance such that a genius often doesn’t add much and can actually make your business weaker by making it dependent on a person (bad for continuity; your shareholders want to keep making money even if one employee leaves). There are, of course, notable exceptions, but they are exceptions that prove the rule (cf. Apple during the times Jobs was with them and not with them).
Applying genius in other places has similar issues, although less so, thus I expect to see more genius in places like academia, R&D, and politics.
I also suspect the reason we work so hard with “physical genius” in the case of professional sports is that there the pressures are great enough to take the risk on genius and make it work as a team. Other places seem to lack the same pressures oddly enough, although I suspect mostly because they are less salient to those who assemble teams. The cases of Xerox PARC and Bell Labs are great counter-examples where someone did recognize the value of making genius work together as a team. But I think most of the time the value of this is not clear or traded off against other things so we don’t take the risks associated with it (remember that most professional sports teams suffer long cycles of poor overall performance and only brief periods of wild success).
Something I don’t think anyone’s said explicitly is that athletic performance is more legible than intellectual performance. If a new breakfast food makes you 1% faster or slower, that’s not necessarily easy to notice, but it’s easier than noticing if it makes you 1% “better at thinking”.
A lot has to do with how dysfunctional universities happen to be. They happen to be places where people go with the intention of learning skills that they can afterwards apply to their work but universities aren’t interested in optimizing for skill development.
Kathy Sierra has a nice talk about how you might actually teach programming if you wanted to optimize for it.
I would say that we already do, in some respects. For example nap pods at Google or the ever common situation of sucking up to all of the needs of a genius, CEO, or prodigy. However I think the real problem is making these services available to more than just the geniuses at big companies with lots of money. Like the way that a neighborhood gym has a weight room, we need mental treadmills in communities too.
We do, a little, when they fight live on TV. Normal people need ideas to become embodied and fight each other. Ideally with narrow margins of victory for excitement. Legibility issues means this is mostly continuous with poiliticking, LCD etc.