The main point is that it’s worth trying to predict technology triggers and asking what those triggers mean. There is value in answering the question so what? A secondary argument is that questions beginning with what if? can be very interesting, but far less reliable than so what? What is a Technology Trigger? The term Technology Trigger is from Gartner’s Hype Cycle. They defined it as: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven. Gartner Research The term has been deprecated in favour of the term innovation trigger. However, as an owner of the hardcover book Managing The Hype Cycle (2008), I[…]

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[…]

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[…]

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,[…]

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[…]

What a fantastic read from Camuffo, Cordova and Gambardella! If you haven’t read A Scientific Approach to Entrepreneurial Experimentation, you’re missing out. It’s a great read. And not only because it reinforces my own preexisting biases, but also because there are challenging bits in there. The core finding is “We find that entrepreneurs that behave like scientists perform better, pivot to a greater extent to a new idea, and do not dropout less than the control group in the early stages of the startup.” The authors focus on a key behaviour that scientists exhibit. A scientist has two types of skepticism – skepticism that something is true, and skepticism that something is not true. Those represent two types of error, helpfully[…]