36 chunks · 32 episodes
...significant amount of investment to load up data (not just a lot of effort, but downside risk from an app that is a bad steward of that data). Huge switch costs! Huge activation friction! Another approach is to lean into being a totally new ki...
A launch is a high-stakes moment with a lot of downside. What happens if you were wrong, and the thing you built isn't actually viable? If you built in a cave, it's hard to get the disconfirming evidence during development to make sure it's strong. Another approach is to develop it in the open, and
A thing that makes it fun to play with your system: users have a rough mental model of "I bet if I did X with Y I'd get Z" and they do it and something interesting happens, even if it's not precisely Z. Especially if there's an undo button or the stakes are low, so the downside for a failed experime
A good thing about a black box: less to worry about, because you can't! The downside is if a black box doesn't produce exactly what you want, you can't tweak it. Black boxes are powerful, but not (directly) controllable. Black boxes are not possible to steer to a better outcome if they don't give th
...ks the user can't blame the creator. That makes the thing more resilient to the downside risk of users having such a bad time that they never use it again. Self-capping downside.
Deciding what to build is easier with fewer people! There are fewer people to align. The effort to align people scales, all else equal, proportional to the square of the number of people. A downside: with fewer people in the group, it's less likely a person with a game-changing idea (or an important
...oth with roughly equivalent short term benefit but one with significantly lower downside risk, they will have a distinct edge to the one with the lower downside risk. This can often be a small but distinct asymmetry, an edge. Ecosystems have s...
...e around it. A proto-aggregator will also have significantly worse potential of downside risk for marginal creators, so if they have no short-term benefit to offer creators, they will never get going. If you already have massive consumer engag...
... a better-than-expected result. The optimal size of the cage has to do with the downside risk. If it's a high downside risk, you want it to be smaller. The bear will be able to do something dumb, but not dangerous. A normal cage is a big cube....
An upside of aggregators is convenience, a downside (for the ecosystem) is control. It's possible to get the upside without the downside.
Robustly tolerable beats precariously optimal when the downside risk is high. Robustly tolerable means a thing that is "good enough" in a diversity of realistic scenarios. It is rarely non-viable. Conditions and contex...
People don't buy a brand because it's good, it's because they know the floor. By buying from a brand you've capped the downside.
..... but there are a lot of ways to blow your foot off! A sandbox makes it so the downside risk is significantly curtailed. This makes it significantly safer (and thus cheaper) for people to explore and experiment and find new pockets of value t...
The downside of thinking you're infallible goes up the closer to infallible you get. When you're very far, it's very clear to everyone that you're not infallible, and they'll take actions to hedge. This hedging creates resilience because it caps the downside. But when you're very close, you start be
..., then don't do the analysis, just do the thing! This is especially true if the downside risk of doing the thing is small and capped. If it's a low cost no-brainer, the bar to clear should be very low. The opportunity cost of analysis, of brin...
Evolution is an amazing powerful innovation force. It's effectively massively parallelized guess-and-check. The downside is it takes a long time and kills most of the mutations in the process.