There are at least two systems of achieving productivity growth: path dependence and disruption. What if there is a third way? This post unpacks that paragraph and explores ways through. It will start with explaining lock in and path dependence. We’ll cover the application narrow machine intelligence in a very narrow industry. It will end with a small scenario and a few what ifs. Lock In Consider banner advertising. This is a relatively old industry. Its roots predate the Internet by at least a couple hundred years. It may have started thousands of years ago. It starts out with a person with a problem. They need to get the word out about their product or service. Reframed, they need to[…]
Category: Economics
Torben Iversen and Anne Wren wrote (1998) “Equality, Employment, and Budgetary Restraint: The Trilemma of the Service Economy” and published it in World Politics, (50), 4, pp. 507-546. And it’s a good read. And you could read it for yourself right here. Here’s a summary in one image: What It Means What causes the Trilemma itself? It’s the idea that productivity doesn’t really grow in a pure local services economy. A restaurant can only serve so many meals, barber cut so many heads, a teacher so many students, a surgeon so many people, a police officer so many arrests. It’s far harder to get compounded year on year growth in productivity in services. As I’ll argue below, it isn’t impossible.[…]
What do you think causes the demand curve? Mechanically, it’s pretty easy to describe the laws of demand. The way pretty lines shift to the right or the left from shocks. It’s possible to deduce the real, rough, shape of the demand curve for a product (It just takes a lot of courage!). We can import all the knowledge about demand, segmentation and price discrimination. We can describe a demand curve just fine. Why does it exist? What causes it to exist? If intelligence didn’t exist, demand wouldn’t exist. It’s fun to think of a machine generating it’s own preferences, independent any human input. Most of human trainers of such machines seem to keep them on a short leash. Monkeys,[…]
What causes conversion? Demand. It’s a simple answer and worthy of unpacking. You could thank Claude C. Hopkins for the simple answer. Hopkins wrote two books towards the end of his life – Scientific Advertising and My Life In Advertising. He seemed to regret his experiences as an agency president, and left some direct advice on how master marketers should think of their choices. In his last decade of life, Hopkins marketed his marketing expertise. Instead of continuing to take on all the risk of marketing product on behalf of somebody else (and maybe getting paid if the product sold), he set up a system where products would be pitched to him. If the product was good, he’d take a[…]
What if Total Addressable Market can’t be estimated accurately? What then? What is Total Addressable Market (TAM)? Total Addressable Market, or TAM, is the number of buyers who are Willing To Pay (WTP) for a solution to a problem they have now, or are Willing To Pay (WTP) your firm instead of the firm they’re currently paying to solve a problem. Why is TAM important? TAM determines the life and death of a firm. The leading cause of startup failure, and perhaps all business failure in general, is the failure to penetrate and/or retain TAM (Including bureaucratic capture and rent-seeking). In this context, I’m concerned about the introduction of a new product into the market in an effort to generate both[…]
Bart Gajderowicz delivered a great talk at Machine Intelligence Toronto about how people go through stages in accomplishing a goal [1]. The talk was about homelessness and AI approaches to public policy. I instantly saw a connection to all sorts of tensions that people endure when they set out on a goal. To distill the concept, let’s start off with the idea that people have goals, people have emotions, and that time moves forward. As people make progress towards their goals, their emotions change over time. They start off in a good mood, in a state of uninformed optimism. Then, as negative information overwhelms their ignorance, they enter into a state of informed pessimism. So much negative information builds up[…]
“A study at Ball State University’s Center for Business and Economic Research last year found that trade accounted for just 13 percent of America’s lost factory jobs. The vast majority of the lost jobs — 88 percent — were taken by robots and other homegrown factors that reduce factories’ need for human labor.” – AP Canada’s labour force is around 19.6 million people, of which 18.2 million people are employed. Together, they worked something like 2.4 billion hours that month. In December 2016, something like 1.7 million Canadians worked about 240 million hours in manufacturing. Roughly. Because of seasonal adjustments and different data at different times. And error. In terms of our working lives in Canada, collectively, manufacturing is about 10% of[…]
This WSJ piece “Has the world lost faith in Capitalism” had this infographic: And prompted Marc Andreessen @pmarca to remark on Twitter: “The inevitable result of 15 years of slow economic growth.” His tweet prompted me to think about the relationship between economic growth and the gini coefficient (a measure of income inequality). And there’s a lot to it. I don’t think it’s a straight line causal model between economic growth and inequality. (And I’m not suggesting that Marc thinks it is, it is, after all, Twitter). The core representation of a causal model is depicted below: In very short terms, when we decided in 80’s that we were going to go for a service based economy, the linkage between wage growth and productivity[…]
Nash died this week, sadly, in a traffic collision. That’s isn’t how I’m going to remember him. My first introduction to Nash was from a book I found in a used book store titled “Game Theory and Canadian Politics”. There was a whole chapter on the Nash Equilibrium and the prisoner’s dilemma in there. This was the diving board for further exploration into game theory and, it was the gateway drug into the Garbage Can, Arrow, and the Genetic Algorithm. He offered up a thread of thought: this idea that collectively, as people, we are capable of creating such sub-optimal outcomes. I didn’t like that one bit. I spent time trying to disprove it. It was Arrow that would end[…]
One application of social technology to a transportation problem caught my eye today: social flights. There are two civilian air travel economies – the public and the private. Private air travel, on private jets from quasi-public air fields, is a completely different experience. For one, there is no invasive security. Delays are fewer. Service is better. The drawback is the cost. Very few people can afford a private flight. Enter the what if. What if the coordination problems among a large group of people could be reconciled? What then? Why, you’d be able to take a private jet with a number of like minded people, at a coordinated time, without delays and harassment – and probably do it for less[…]