Everything important to talk about, like Black Lives Matters, is so difficult to talk about nowadays. So let’s take a break. Twitter sometimes serves up some interesting stuff. And one of the people I follow — Erik Hoel, a literal bright young mind in the world of evolution of consciousness — served up the graph below on Moore’s Law from this book.
Moore’s law says that the number of transistors on a given chip will double almost every two years — exponential growth. Well, for a while. Over time, though, you’ll get more of a logistic curve, which is what you’ll see when you start saturating an environment (or technology). It’s the way exponential growth usually ends. And it is a phenomena primarily of systems modeled initially with linear equations.
It should come, therefore, as no surprise (though I can’t believe I hadn’t framed it that way!) that any given tech. ends with that flattening off that is inherent in such phenomena. Erik’s observation was that this was indeed a logistic curve. And I think he’s right. I also KNOW I’m not the first person that’s observed this, but this is a quickie post, and I’m not going to go back and resolve this.
That actually maps into the social structure/knowledge part of the multiverse as follows. A field is founded, from whatever value set/meme generates it. Scientific hierarchies — fundamentally, social structures that produce knowledge in meta-linear fashion – are set up to study it. Progress is rapid at first, but specialization, followed by microspecialization, occurs, as the hierarchies churn out increasing sophistication of knowledge, with more fine-scaling, but with little actual positive affect. The hierarchy is actually necessary for that sophistication — there’s only so much one can do — but as the field solidifies/rigidifies, status in that field is set up through well-known status triggers (if we could only solve Fermat’s Theorem!) and more and more people pursue what in the end will turn into a refinement rabbit-hole.
This goes along until some alternate horizontal break-through occurs, almost always from outside the social system that created the original tech. refinement. What that might look like is often a nonlinear shift, and created by a handful of individuals. The trajectory of displacement needn’t always be a jump in performance, or fundamentally disruptive — it can also exist of a budding/parallel technology. Then the process of evolution leads once again to the establishment of hierarchical social structures, with the same branching refinement and sophistication all over again. Roger Martin won’t ever admit to the social structure part, but this is basically on a macro-scale what he discusses how to avoid in his book The Design of Business. Though a bit simplistic, I love this graph of his. Note the collapse of complexity over time — as well as the ability for a diverse, heuristically oriented team to make a difference. Follow the damn rules, please!
For those that need a refresher on the Complexity Evolution vs. Sophistication structure stuff, you can go here. Or you can think about this plot below.
So much of this is queued on social structure, and what is interesting, is that this type of knowledge evolution used to be captured well inside university systems — mostly because we just didn’t know as much, from a refinement point of view. Some bright, likely egocentric individual could chase a particular thought, looking for their muse, and create a disruptive breakthrough. But as knowledge evolves, and breakthroughs are more dependent on cross-disciplinary stimulation, these types of situations became harder and harder to replicate. Especially in the walled up silos of a university.
So universities, still as institutions seeking overall status, encourage more chasing of sophistication. And that sophistication requires more work, with less time for original ideas. And more worker bees, with less and less money per worker bee, along with more and more direction from money from the outside, often from minimally creative bureaucracies, like the National Science Foundation, whose natural memetic tendencies favor incremental refinement. And so on. This is supposed to be a Quickie Post, after all!
What is so fascinating is that the natural behavior of such a social structure shows up so clearly in that aggregate graph, that literally reflects the work of millions of people. (And yes — I do know that not all microelectronic research happened in universities — but the same principles hold.)
What’s the short take? Nothing we haven’t discussed on this blog already. If we can’t get to truly novel takes, statistically, from the depths of one genius’ mind, let’s mix things up a bit, and form teams of interested, curious people, with enough differentiation through standard measures of diversity, as well as understanding the influence of cognitive diversity through structural memetics. Let’s see if we can embody in our institutions principles that match the social physics, instead of the endless status-chasing of the US News and World Report. And let’s make sure people have enough free time so they can actually breathe.
There’s nothing wrong with chasing the next doubling of transistor density. But make no mistake — all good things must come to an end. It’s in the social/information physics.
One final thought — what happens to those rigid hierarchies, left alone to their semi-infinite Pirate Pugg mendacities? Well, they drown in a sea of irrelevant paper tape. But it DOESN’T have to be that way. The rigid social structure of hierarchies inexorably marches in that direction. But evolved leadership can see those forces, and through conscious interventions, feed their specialists on a diet of diverse influences, so they can continue to grow. For me, I did this with things like chairmaking classes, or working with underserved minority communities in STEM. It ain’t free — there has to be some free energy in the system for people to think about something else. But it definitely increases your odds that someone inside your organization will create the next breakthrough.