The "TEN" of this site previously stood for "The Entrepreneur Network". It was a site through which I counseled entrepreneurs, inventors, technical service providers, artists, etc. interested in bootstrapping their own venture for 30 years (see bio). I took that content down in 2014, as entrepreneurial interest in bootstrapping in the U.S. had pretty much disappeared. The questions I was getting were mostly, "Where can I find Venture Capital?" — which I felt (and still feel) is a losing game for most entrepreneurs.
In 2016, watching the exponential growth in digital technology (that I had been a part of since punched-card days) and fearing what that would do to the job markets1, I subscribed to The Economist magazine to see how economists were planning to deal with the impending job loss. To my amazement, there were no plans — in fact, near zero recognition of the problem at all. So I became active in the magazine's Comments section, trying to call attention to the urgency of the problem.
Over the next few months, browsing the technical and economic literature, I came to realize the urgency was less than I had feared. Although the technical capability exists to create massive job losses (which it eventually will), businesses are very slow in adopting it. Business people not immersed in the technology are maddenly slow in putting it to use — and most technical people don't have the understanding (or interest) in pushing them to use it more fully. In short, a massive communication gap is dramatically slowing the potential job loss.
But I had now picked up an interest in macroeconomics — and started seriously reading many of the economists' blogs and papers. But the more I read, the more disillusioned I became. Having no previous introduction to economics, I initially assumed economists were scientists (and that was reinforced by the math I was seeing). But as I read their blogs and papers and worked through many of their mathematical models, I came to realize they're not scientists at all — but philosophers. A model reflecting reality simply CANNOT be built from variables that cannot be precisely defined and accurately measured — so their models are essentially useless. They may give insights (for readers to test with their own logic), but any notion that they're offering "truths" is just simplistic.
And to compound that weakness, most economists' papers share the academic weakness of trying more to disprove other economists' work than offer viable solutions to real-world problems. So much of what is found in today's economics textbooks and blogs is simply false — the real-world truth often being the exact opposite of what is written. Economics is not that complicated — little more than the disciplined logic every human is capable of.
So that's how this site has become The Economic Novice. If anyone wants to contact me, I'm available at edzimmer@TENonline.org.
1 I see human intelligence as a combination of memory, deductive reasoning and inductive reasoning. Computers have proven to be superior to humans in memory and deductive reasoning. They are currently incapable of performing inductive reasoning and, to date, I've seen no credible approach to endowing them with that. Such a breakthrough MAY come from current neural research, but so far it hasn't — not even the beginnings of an understanding of inductive reasoning in the human brain. That's not to say that the current efforts in machine learning will not have revolutionary impact on society. Most paid work requires little inductive reasoning — and what little is required can be provided by one human in cooperation with multiple robotic entities. So it's virtually certain we're facing an irreversibly shrinking job market. A counter-argument is that the technology will produce new kinds of paid work (as it has in the past) — but in that case, the basic kinds of such work should be describable — and, most importantly, an explanation offered as to why this new work isn't as susceptible to automation as was the old work. And to date, I've seen no one attempt that.