A colleague recently emailed an argument that higher level computing providers like Heroku are doomed due to the "Fundamental Law of Another Mouth to Feed" where the margin requirements of the services these providers are reselling means they will eventually lose to someone without that margin in their supply chain. As with any asymptotic complexity argument, this is sound theoretical thinking, except it ignores the significance of constants in the equation. Following is a slightly cleaned up version of my response.
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The Fog of War is invoked to describe the uncertainty that permeates combat operations. This term is generally attributed to a quote by Clausewitz: "The great uncertainty of all data in war is a peculiar difficulty, because all action must, to a certain extent, be planned in a mere twilight, which in addition not infrequently — like the effect of a fog or moonshine — gives to things exaggerated dimensions and unnatural appearance." (via wikipedia).
The evolution of an innovation or innovative company faces a similar fog. At any point in time, we extrapolate from current conditions a set of possible future outcomes and take action to try to bring the best outcome into being. In one view, we should make economically rational decisions, those that maximize our expected return over the outcomes weighted by their likelihood.
However, Nassim Nicholas Taleb's "The Black Swan" argues convincingly us that the seeds of disruption and destruction lie in events that exist outside all reasonable extrapolations of a current state of affairs. The occurrence of a highly improbable event is, in fact, inevitable.
Since we can't know anything for sure, innovators have to operate at a completely different level than analysts. It's a chess game where the rules can change mid-play forcing a complete re-evaluation of strategy. For example, the iPhone was a Black Swan to the cellular industry, as this analyst's perspective shows. This dynamnic is why you should ignore Forrester and other analysts - they tell you what the world will be like if nothing surprising happens, but surprising things always happen.
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The family doctor or nurse's job is an impossible one. Given our imperfect description of symptoms, and possibly a blood test, they have to figure out what intervention will help us return to a stable, healthy life. The problem is that human beings are probably the most complicated system that we can imagine to try to regulate. Our environment, psychology, and life habits interact deeply with dozens of major internal organs and body systems which manifest tens of thousands of possible known problems. We know a great deal about the components of our body, but what we do know is dwarfed by what we don't know about how these components interact with one another. After a brief digression into what makes medicine a hard problem, I'll introduce some ideas for what we, as patient participants, can do to improve the problem solving process, our own care, and healthcare at large.
Network Medicine
A recent TEDMED talk by Albert-László Barabási highlights the rich system of interconnected networks the exist both within and outside our body; networks are a way of viewing the world that we are only beginning to understand. The functioning of our body is not independent from our mind and environment. The bugs in the soil communicate with the symbiotic bacteria that enable us to digest food, the micro-RNA of the plants we eat may directly change our gene expression, the inactive ingredients in breast milk line a baby's intestines and protect it from external pathogens until the immune system has matured, and our expectation of a treatment's effect can physically transform how our bodies function (the so-called placebo effect).
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Recently I've been observing via RescueTime that I spend 3 hours or more hours in my e-mail application most days. However, I don't have a good breakdown of how much of this is scheduling, looking up information, commenting on something substantive or social discourse. There is a tremendous amount of information locked up in the time-series of e-mail's sent and received that can provide insight into aspects of my behavior such as focus of attention (time of day e-mail is sent), social relationships (what organizations I interact with in a given week), the density of idea generation, etc. E-mail logs contains a wealth of raw data that can be instrumented to uncover important information about our life.
Our E-mail logs are also rich archive of useful information such as phone numbers, addresses, what we said to someone, when we said something to someone, edits to papers, attachments, etc. With a proper set of tools, many of which have been built for analyzing social media, we can turn this archive into a database of useful information that can significantly enhance e-mail-based instrumentation.
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It has been gratifying to recieve some great press coverage of C3N; the ideas we are exploring are clearly starting to capture the imagination of a larger audience. However, C3N is a larger and far more collaborative project than the articles imply and this post aims to provide some detail from my own perspective, expanding on my earlier introduction.
Origins of C3N
C3N is a platform in the broadest sense. It consists of human, process, and technology components. It was initiated by a transformative research grant from the NIH Director's Office in 2009, the result of a proposal developed by Jesse Dylan of Lybba, Peter Gloor of MIT, Neil Halfon of UCLA, and Peter Margolis and Michael Seid of Cincinnati Children's Hospital and Medical Center. The inspiration for the effort emerged largely from Jesse's vision of bringing patients into the care system as equal partners and Peter Margolis' experience developing large networks of clinicians that standardize care and apply process improvement techniques to optimize care and improve outcomes for pediatric patients with Inflammatory Bowel Disease in the ImproveCareNow network.
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A discussion I had earlier today reminded me of an argument I've had with friends in the scientific community on multiple occasions. The argument revolves around the belief that conclusions of science, such as the effect of cholesterol on heart disease, suggests specific interventions, such as reducing the dietary fat that we believe causes high cholesterol. In essence, we debate the means by which new scientific evidence should be used to influence public policy and private behavior. Taking strong evidence of a specific causal link between a cause and an undesireable outcome as prescription for a population intervention to remove the causative factor is fraught with danger. There are many reasons for this, but the two most salient are confounding and the law of intendended consequences.
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My github fork of the Clojure library for HBase, clojure-hbase is now deprecated. I've extracted the functionality from David Santiago's original library (with permission) along with a duplicate of his admin functions to create a parallel repository with the schema-oriented API I developed.
The new repository is owned by Compass Labs and can be found here. The library can also be referenced via Maven / Leiningen:
com.compasslabs/clojure-hbase-schemas "0.90.4"
David Fore of Lybba forwarded me this article on innovation. It provides a set of beautiful examples of the role of discipline and standardization in fostering innovation.
At my first startup company, Silicon Spice, we were extremely disciplined in how we structured internal communications, identified and followed technology standards and incorporated automatic testing into all our technology development efforts. The company-wide discipline we established allowed for an unprecedented pace of product innovation in the rapidly evolving communications market of the late 90's. Peaking at only 120 employees, we constructed a complex set of technology targeting telco equipment vendors including reference voice switching boxes, a 2.5 Watt 21-core DSP processor, companion ASIC devices, a vector compiler, a real-time OS, a fully-featured set of voice processing software and a complete suite of development tools for our platform. A few people in key roles were able to spot opportunities that spanned numerous product sub-teams and we were able to quickly implement coordinated design changes across our home-grown technology stack.
A lead engineer from Texas Instruments, our primary competitor, once commented to me that they couldn't believe that we had build our product in only 3-4 years with 100 people; I didn't have the heart to tell him the product they were evaluating for acquisition was built by an average of 50 people over 16 months! We seized 70% of the carrier-class market from them in the 18 months following our acquisition.
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