This is post is the first in a five part series on Capital, and You.It’s written for people who turn data into product and who may have some questions about why they’re seeing what they’re seeing and why they’re feeling what they’re feeling at a startup. The first post explains what is Capital. The second post attempts to explains the relationship between Capital and the Venture Capitalist. The third post explains the relationship between the Board and the Venture Capitalist. The fourth post expands the relationship between the Board and the Founder. And fifth post expands on the relationship between Capital and the Citizen. Capital And You: What Is Capital Capital is potential power. In order for capital to make[…]

Previously, I asked what kind of leader you wanted to be. In it, I struggled with the question of the tradeoffs of misrepresenting ground truth. Suppose you work at the WWE and you need to make a business decision. Somebody literally believes that it’s all real. You need to make a business decision about a contract renewal. What kind of harm are you doing to them, and to yourself, in going along with their belief, something that you know yourself not to be true? Absurd? Yes. Beliefs are absurd things. Later that month, I was asked why I was so sure that WWE wasn’t real? Why was my version of ground truth any more legitimate than somebody else’s ground truth?[…]

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 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[…]

The whole thing, all of it, depends on optimism. Optimistic expectation is a natural force generated by humans and amplified by the networks that humans create. At the core of the entire traditional liberal paradigm, since the enlightenment, is the expectation that things will be better in the future. If things get better at a rate of just 2% per year, compounded annually, things get twice as good in just 35 years. If things get better at just a little bit more than that, 3.6%, things get twice as good in just 20 years. We’ve come to expect things to become better, dependably, and predictably. The enlightenment is an important event to call out. It’s way easier to shrug ones[…]

You build three machines when you build a startup. Your ability to build these three machines is the Great Filter to your life in the business universe. This post is an effort to describe why some startups fail, why some are small, and why others grow big. The Great Filter The Great Filter refers to a concept that Robin Hanson came up with to explain why we don’t see any evidence of intelligent life in the Universe. One can get a better sense of different scenarios when one considers how many things need to be true for intelligence to emerge, and assigns probability to them. If it’s the case that the coincidences required for life to occur are exceptionally rare, then[…]

Teams, in software engineering, form because of success. Without success, the firm wouldn’t be cursed with the problem of having so much talent to have to organize in some way. A founder can easily reduce the complexity in their human organization, and their lives, by simply not hiring any more than seven technologists to work with them on their mission. For some, this is viable. For others, this is not. Teams emerge in response to scale. They are either formed as by product of centralized hierarchical command structure, or they emerge as a product of network cohesion/polarization. To the extent that either formation is aligned with the vision, goal, mission, or purpose of the organizational chrome is a function of[…]

Imagine with me: what if novels were written like software. Sometimes it’s useful to approach absurdity and look inside. There might be treasure there. I’ll define software as an executable, a set of instructions, that are interpreted by a machine for some reason. As a data scientist, I think of software as a product, and I think, constantly, of turning data into product. I think of data as inertia and all the code around it as flexible. I worry a lot about the people that use the software (if anybody) and think of them as heterogenous segments. I think of a novel as an executable, a set of instructions, that are interpreted by a human brain for some reason. As[…]

This is a dense post. Feldman and March, in 1981, wrote “Information in Organizations as Signal and Symbol”. And it makes good predictions about what a management scientist type would say about the purpose of information in an organization. Indeed, just last month, I hyped Carl Anderson’s 2015 original position yet again, in the framing of information as assisting learning. Feldman and March are cited by another piece that’s been weighing heavily since February. Alvesson and Spicer’s 2012 hit “A Stupidity-Based Theory of Organizations” explains why seemingly intelligent people pretend to be dumber than they are. Please don’t misinterpret this passage. It’s not the case that everybody is stupid. Sometimes people act dumber because they have to go-along-to-get-along. Are you[…]

This post describes a fast follow startup and the implication for how that startup learns. Define Startup A startup is a market hypothesis looking for validation. It’s an organization in search of a business. If they’ve accepted funding, then it’s a group of people looking for a liquidity event. Define Follow Follow means imitation. It means that an entrepreneur or a herd entrepreneurs have been observed pursuing a particular product-solution-market fit, or a hypothesis, and some founder wants to join the herd. Define Fast Fast means that the organization is imitating fast enough to nip at the heals of the lead innovator. It is imitating fast enough to be contention of overtaking the leader, or close enough to experience a[…]