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Rethinking Polymathism

Aristotle is described as being the last person in human history that probably knew “everything”. He was a pioneer of knowledge in a wide array of fields including physics, biology, zoology, metaphysics, logic, ethics, aesthetics, poetry, theater, music, rhetoric, linguistics, politics and government. Others have since then have had that title ascribed to them. For example, Thomas Young in the early 1800s demonstrated that light is also a wave, did work in linguistics and Egyptology which later helped researchers decode the Rosetta Stone, proposed the tri-chromatic theory of vision, read or spoke at least 11 languages and played the flute. However, the CV of most recent of popularly described polymaths are quite a bit shorter. It seems in today’s world, the body of human knowledge has grown so large that no single individual can “know it all”. Most modern polymaths, like physicist Freeman Dyson or former Microsoft CTO Nathan Myhrvold seem to exist largely in a professional silo with writing or product development products that are related to their prior knowledge or work.

This shows some very interesting things that might be useful to take into account as you plan for your future and think about the trajectory your career could take.

  1. Go deep. In a world with a billion people, Thomas Young’s world, the competition one would face in their pursuit of any given field will be much lower than it would be in a world with 7.4 billion people. This affects some fields and industries more than others. (It’s unlikely that anyone is going to write Harry Potter before JK Rowling or that Benedict Cucumberpants will miss all opportunities for a breakthrough performance.) However, this does suggest that you must be mindful of the fact that in any given field in which you wish to be a generalist, you are guaranteed to be facing a swarm of specialists. This doesn’t mean you can’t still add value by combining knowledge in new ways, but it’s important to be aware of. If you don’t find an area to gain deep knowledge in, you run the risk of becoming an intellectual dilettante. Someone who’s is very impressive at party banter, but incapable of building anything with their knowledge.
  2. Find the overlap. Successful modern polymaths seem to find their best success by building off of their core knowledge. For instance, Nathan Myhrvold has intensively studied math and physics. He’s also a very well known chef and cookbook author. However, his expertise didn’t come from a fancy culinary school. He applied his knowledge of physical phenomenon such as thermodynamics to study how food and the chemicals that make it up can be manipulated. Another example: Richard Posner is a judge who has studied economics extensively and was a pioneer in anti-trust law. He now writes on a wide variety of subjects like history and human sexuality, but his analyses always take an economic approach. These thinkers find the edges of their field and figure out where it overlaps with other interesting fields.
  3. Business and action are your best weapons. One thing you’ll notice as you read about polymaths of the past 100 years is that they are often described with initials. PhD in this. MS in that. MBA, JD, MD, EIEIO. The real basis for evaluating someone’s success as a polymath has been institutional. Not only that, as Richard Posner from above points out:

    “Even in relatively soft fields, specialists tend to develop a specialised vocabulary which creates barriers to entry,” Posner says with his economic hat pulled down over his head. “Specialists want to fend off the generalists. They may also want to convince themselves that what they are doing is really very difficult and challenging. One of the ways they do that is to develop what they regard a rigorous methodology—often mathematical.
    “The specialist will always be able to nail the generalists by pointing out that they don’t use the vocabulary quite right and they make mistakes that an insider would never make. It’s a defence mechanism. They don’t like people invading their turf, especially outsiders criticising insiders. So if I make mistakes about this economic situation, it doesn’t really bother me tremendously. It’s not my field. I can make mistakes. On the other hand for me to be criticising someone whose whole career is committed to a particular outlook and method and so on, that is very painful.” -Richard Posner

    However, with the Internet, many of the industries that Posner is speaking of have been flattened. The gatekeepers, those who might try to keep you from participating in an industry by developing artificial barriers, can’t keep you out. Do you think “industry standards” have stopped Elon Musk from building spaceships? Not anymore. And it won’t stop the companies following in his wake building satellites and new ship prototypes. Want to publish a book? The opinions of the New York Times or even publishers don’t matter much when you can self-publish and sell millions of copies. In short, the most wild forest of obscure professional jargon and certifications can’t stop someone who proves the value of their ideas in the marketplace.

  4. Be curious and don’t waste time. The nature of polymathism may have changed, but this remains the same. Those brave pioneers who can successfully transcend the boundaries of a single field must not only remain curious about knowledge outside of their realm, but the must also use every moment as fuel for their curiosity. There is too much knowledge to gain to stuff it in between Netflix episodes.



Open Companies as Engines for Basic Income

Y Combinator is currently wanting to experiment with Universal Basic Income. I think it’s a laudable goal.  I find the concept of “Universal” very problematic, because it inherently divides the concept from utility or results and turns it into yet another screechy, emotional battle cry for redistribution, but from what I’ve read about human motivation, I don’t believe that implementing a basic income for an individual will necessarily rob them of all desire to work or create or succeed. I think that people with a survival mindset actually produce less value, because their minds are constantly in a state of fear. So, I support the idea of BI from a utilitarian perspective. I don’t think anyone has the rights to the fruits of another’s labor, but I do think that to achieve higher levels of production, happy, healthy people are needed.

So, this leads us to a very exciting laboratory. If we assume from the very beginning that need is not a claim on the wealth of another, then how can we create voluntary systems that alleviate the survival mindset.

One rough concept that I’ve been chewing around in my mind is that of the open company. Let’s take the Morningstar Tomato Canning Company which is an organization that is extremely distributed in its management structure. In this company, individuals work together by making contracts with those that work around them. Tenure at the company is based on those contracts as no one has the authority to fire another person. I’ve heard the former CEO of the company, Paul Green Jr., mention that they were 70-80% there as far as having a completely flat organization.

What would a completely flat company look like, though? No one is managing production, and everyone is responsible for the financial success of the business, so the business could grow in any direction. It could horizontally integrate. Vertically integrate. Bundle. Unbundle. It would almost become its own supra-nation operating at a different level of human activity. There is no central hiring authority, so people are hired at the edges where their contracts dictate. This I think is an interesting model for working with BI.

What if every node in the network had the authority to distribute some dividend of the company’s output? Central nodes have a greater amount of dividend to distribute and more isolated nodes have less. Or, what if every node had the ability to distribute some amount in proportion to the value they created? Percentages and amounts could be determined via weighted liquid democracy, or internal accounting safeguards built into the laws of the company. For instance, “Cash reserves can be no lower than $X of annual operating costs.”

Under a model like this companies become organic value creating networks operating under their own economic legal framework. All distributed assets are not only tied to value creating activities, but presided over by them. This empowers local parties to manage distribution and prevents external parties from consuming the economic model.

There are so many tweaks that could be made, and, as long as it’s left to the devices of entrepreneurs and innovators and not bounded by bureaucrats, I think incredible models for creating happy, value-oriented communities could arise.


Value Potential Graphs via Machine Learning

After watching a few videos on machine learning, I feel like there’s a very, very large opportunity in using it for what might be known as “job placement”. I put that in quotes, because I think in the future that concept as we know it now might be pretty foreign, because I don’t think it will be divorced from education, and I don’t think it will be as structured as it is now.

It seems that whenever we, and this is potentially me projecting here, discuss something like job placement it has a very structured, top down feel. A central body evaluates you and points you somewhere else. However, if there were instead a machine learning network in place, you could perhaps do monthly little learning boot camps and provide feedback on how you enjoyed it, and be served feedback on how quickly you picked it up. That would turn an aspect of your skillset into labelled data allowing your self to be further sorted towards value activities of best match.

Machine learning isn’t wholly a top down assignment. Semi-supervised machine learning is internally assigned values, spot-checked by participants, surfacing patterns in the data and giving you a better idea of where you can offer the most value to the world.

What if “higher education” was simply an iterative process of labeling your progress in an internally consistent machine learning network.

Then, imagine this in a world where many companies had their hierarchy mostly distributed. That means no HR department. That means a “Value Potential” graph introduces you to someone at a company and you make a contract with that person to do work for their company. This kind of completely distributed corporate hierarchy exists, and I can only imagine it will grow in popularity.

Everyone’s afraid of AI these days it seems, but what if our entire concept of what AI can do for us is wholly tainted by top-down way our societies have been managed for hundreds of years. It’s hard for us to think of anything else. But, absent of centralized state control, what can AI order us to do? Really, in that scenario, general AI isn’t a commander. It’s a tool for us to find our local maximum. To gain perspective on where we are in the mix. To turn each of us into the most informed decision makers we can be.