Weekend Reading: Data Moats, China, and Distributed Work

May 25, 2019 • #

🏰 The Empty Promise of Data Moats

In the era of every company trying to play in machine learning and AI technology, I thought this was a refreshing perspective on data as a defensible element of a competitive moat. There’s some good stuff here in clarifying the distinction between network effects and scale effects:

But for enterprise startups — which is where we focus — we now wonder if there’s practical evidence of data network effects at all. Moreover, we suspect that even the more straightforward data scale effect has limited value as a defensive strategy for many companies. This isn’t just an academic question: It has important implications for where founders invest their time and resources. If you’re a startup that assumes the data you’re collecting equals a durable moat, then you might underinvest in the other areas that actually do increase the defensibility of your business long term (verticalization, go-to-market dominance, post-sales account control, the winning brand, etc).

Companies should perhaps be less enamored of the “shiny object” of derivative data and AI, and instead invest in execution in areas challenging for all businesses.

🇨🇳 China, Leverage, and Values

An insightful piece this week from Ben Thompson on the current state of the trade standoff between the US and China, and the blocking of Chinese behemoths like Huawei and ZTE. The restrictions on Huawei will mean some major shifts in trade dynamics for advanced components, chip designs, and importantly, software like Android:

The reality is that China is still relatively far behind when it comes to the manufacture of most advanced components, and very far behind when it comes to both advanced processing chips and also the equipment that goes into designing and fabricating them. Yes, Huawei has its own system-on-a-chip, but it is a relatively bog-standard ARM design that even then relies heavily on U.S. software. China may very well be committed to becoming technologically independent, but that is an effort that will take years.

The piece references this article from Bloomberg, an excellent read on the state of affairs here.

⌨️ The Distributed Workplace

I continue to be interested in where the world is headed with remote work. Here InVision’s Mark Frein looks back at what traits make for effective distributed companies, starting with history of past experiences of remote collaboration from music production, to gaming, to startups. As he points out, you can have healthy or harmful cultures in both local and distributed companies:

Distributed workplaces will not be an “answer” to workplace woes. There will be dreary and sad distributed workplaces and engaged and alive ones, all due to the cultural experience of those virtual communities. The key to unlocking great distributed work is, quite simply, the key to unlocking great human relationships — struggling together in positive ways, learning together, playing together, experiencing together, creating together, being emotional together, and solving problems together. We’ve actually been experimenting with all these forms of life remote for at least 20 years at massive scales.

Topics:   weekend reading   data   machine learning   China   trade   remote work   work