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 if code is an artifact of the culture that creates it? What would your interpretation of the code suggest to you about the culture? What would different layers of code tell you about how people lived in the past? Culture Code is instructions to be run by machines and interpreted by the humans that take care of it. So much code is managed by people. And groups of people get to together and create language, standards, rituals, traditions, meanings, arguments, rhetoric, procedures, regulations, obligations, agreements, memoranda of understanding, specifications, memes, stories, and values. Cultures evolve. For instance, as a startup goes from 2 people to 5, then 5 to 11, (11 to 23, 23 to 47, and so on)[…]
The inspiration for this post is John Cutler‘s excellent twitter thread on prioritization. It’s well worth the read. This post builds on that inspiration using Roger Martin’s concept of the The Knowledge Funnel. One big takeaway of John Cutler’s thread is when deciding the sequence of what to do in product management, consider the big picture and think of the impact of what you will do next on what you will know next. What I like about Roger Martin’s concept on knowledge funnels: consider the big picture and think of what you know about value. Product management and data science is all about managing the knowledge funnel. Your ability to manage this funnel is predictive your ability, and those you[…]
The Knowledge Funnel is a concept introduced by Roger Martin in Design of Business: Why Design Thinking is the Next Competitive Advantage (2009). The book is excellent and worth a read. There are mysteries at the top of the funnel. Mysteries are the unknown. They’re the known unknowns and the unknown unknowns. It’s knowledge that the organization doesn’t have. In the centre of the funnel, you have heuristics. These are rules of thumb. They aren’t quite always precise, and aren’t always quite reliable. Heuristics are just predictive enough to be useful. It’s knowledge that is known to the firm. At the bottom of the funnel you have algorithms. This is knowledge that is standardized and optimized enough to be run[…]
There are many calls to break up tech. Break up what, exactly? Regulate tech? Regulate what? There’s a lot of polarization about what to do about Facebook, Amazon, Apple, and Google. That polarization is in part driven by anger. Dig a bit deeper and see fear. Maybe you’re feeling it. Here’s how I see it. The Assumptions People are heterogenous. Peoples’ beliefs are heterogenous. Peoples’ willingness to believe are heterogenous. Peoples’ inventiveness and imagination are heterogenous. Peoples’ willingness to tell or repeat stories are heterogenous. Peoples’ susceptibility to stories, and to storytellers, are heterogenous. Peoples’ need to belong are heterogenous. People form networks because they need to belong. Information (Gossip, facts, stories) is transmitted along those networks. These variables (information,[…]
On the walk in, I thought of Livy, of networks, and of hierarchies. I thought of Samuel Doe, the Liberian that brought an end to the brutal True Whig Party and brought in his own brutal regime, and of Emperor Bokassa, who once spent a third of his nations budget on his coronation. They each grew up physically, yet never had a chance to develop fully as whole people. And because they were leaders, the societies they led never got the chance to develop either. Are systems really that sensitive to leadership? Can it really be that institutions are that sensitive to the development of their leaders? The thought rocked me for a few minutes, and then subsided into an[…]
Suppose the following scenario: Series A or B; A data science firm (narrow machine intelligence, applied machine intelligence, general machine intelligence, predictive or prescriptive analytics, software or hardware); Technical CEO / Co-Founder; Chief Marketing Officer (CMO) just hired; What might the CEO-CMO relationship look like? The relationship could be great. If there’s one stereotype about data science CEO’s, it’s that they like incentives to be aligned. The CMO would likely be brought on to focus on growth. If revenue grows, valuation grows, and collective comp would grow. There might be points of friction. From the CMO’s Perspective: Why is the CEO constantly at me about metrics all the time? Why is the CEO always on about non-working dollars? (Why don’t[…]
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,[…]
It seems like a lot of people value certainty. People buy a lot of products and stories for certainty. Insurance. Investment advice. Forecasts. Indulgences.Many entrepreneurs, in particular those in data science, sell certainty. What else is an F1 score other than a measure of certainty on some level? Given some inputs, our machine transforms them some way, which produces some statement about the past, present, or future, with some quantifiable amount of certainty, so that you can do something with confidence (or feel more secure). We sell certainty. And yet isn’t it curious about how much insecurity we’re creating while we do so? It has always been easier to sample data from the past, pull a heuristic from it, and[…]
This is post is the fifth in a five part series on Capital, and You. Previously, I defined capital as potential power, and argued that the primary optimization objective of the venture capitalist is to acquire more capital. Further, the board is the embodiment of the Corporation, it is made up of people who represent the Venture Capitalist, the Founder(s), other shareholders, and by proxy, Capital, and it is obligated to behave in a manner that increases capital accumulation. If the Board and the Founder are aligned in the pursuit of increasing capital, great capital may be accumulated. If they are not, doom. This fifth and final post expands on the relationship between Capital and the Citizen. Should citizens of[…]