Weekend Reading: COVID Edition
Benedict Evans looks at what could return to normal after coronavirus, and what else might have accelerated change that was already happening.
“Every time we get a new kind of tool, we start by making the new thing fit the existing ways that we work, but then, over time, we change the work to fit the new tool. You’re used to making your metrics dashboard in PowerPoint, and then the cloud comes along and you can make it in Google Docs and everyone always has the latest version. But one day, you realise that the dashboard could be generated automatically and be a live webpage, and no-one needs to make those slides at all. Today, sometimes doing the meeting as a video call is a poor substitute for human interaction, but sometimes it’s like putting the slides in the cloud.”
One of the things continually aggravating about all of the data, models, projections, and analyses about COVID-19 is how little anyone cares to retroactively analyze prior predictions. Over the last two months the predictions have been all over the map, and as time marches on and many are wrong, some are right, there’s no analysis of what assumptions were made that turned out not to be true causing the wide divergence between projection and reality.
Peter Attia calls out here something rarely acknowledged about why projections are wicked:
“Projections only matter if you can hold conditions constant from the moment of your prediction, and even then, it’s not clear if projections and models matter much at all if they are not based on actual, real-world data. In the case of this pandemic, conditions have changed dramatically (e.g., aggressive social distancing), while our data inputs remain guesswork at best.”
Nassim Taleb, making his way into the New Yorker.