Social vs. Science Experiments

What makes us messy also makes us magical. Social experiments are every bit as important as scientific ones, even if they make for a bumpy ride.

While science experiments often seem more straightforwardly and obviously good – a new life saving medicine or cheaper electricity solve clear problems – humanity’s uniqueness, and our ability to solve hard scientific problems in the first place, comes from our ability to coordinate and work together as social creatures. Democracy was a bloody, mes... See more

[in bear markets] I think the bigger thing is that the total lack of interest from the outside world lets social experiments mirror science experiments as closely as possible.

That gives the product the chance to be as much like a science experiment as it’s possible for a social experiment to be, making and fixing mistakes in private with the collective input of people who care about the product. It will be big one day, and maybe there will be a token, but by building in a semi-closed lab, it should have a strong enough ... See more

Start with a small niche, grow the density and connections among participants, and only then start adding on adjacent networks. That’s really hard to do in a bull market, when a bunch of people rush anything that looks even remotely promising, diluting and even poisoning the niche, particularly when web3 products are designed to be open and permiss... See more

Social experiments live and die by Network Effects, the prize for dealing with all of the human messiness upfront for the few social experiments that make it to the other side. Network effects can become so strong that it doesn’t matter how bad the bits a company produces are, people will stick around (ahem, Facebook).

After a bunch of tedious, incredibly difficult work in relative privacy, these products are so obviously useful that adopting them is becoming a no-brainer. That’s one of the benefits of quietly doing science experiments in the lab before commercializing.

That’s not to say that Hype doesn’t impact public perception of science experiments. AI was famously always “ten years away” and “just linear regressions with a fancy name”... until it wasn’t. But AI’s success didn’t depend on getting a lot of people to use not-yet-good-enough chatbots; it depended on keeping a relatively small number of nerds exci... See more

Metcalfe’s Law, which states that the value of a network is proportional to the number of people using it, cuts both ways for social experiments that need to establish network effects. It’s really hard to get the right people to use the right product in the earliest stages, and most network businesses fail because of this Cold Start Problem.