Solving All the Wrong Problems

Solving All the Wrong Problems
nytimes.com

The problem is: According to Malcolm Gladwell - The Tipping Point (https://en.wikipedia.org/w/index.php?title=The_Tipping_Point...) there are three kind of people that one have to reach to become viral (cf. https://en.wikipedia.org/w/index.php?title=The_Tipping_Point...):

"Connectors are the people in a community who know large numbers of people and who are in the habit of making introductions. A connector is essentially the social equivalent of a computer network hub. They usually know people across an array of social, cultural, professional, and economic circles, and make a habit of introducing people who work or live in different circles. [...]"

"Mavens are "information specialists", or "people we rely upon to connect us with new information".[4] They accumulate knowledge, especially about the marketplace, and know how to share it with others."

"Salesmen are "persuaders", charismatic people with powerful negotiation skills. They tend to have an indefinable trait that goes beyond what they say, which makes others want to agree with them."

Unluckily the groups in the quote above do not fall into these three classes. The problem really is if you start a startup you make a bet that in a rather short time (until money runs out) you will reach a critical mass of people for the startup to become self-sustained (or at least get enough customers such that you can get further funding). Obtaining such a critical mass in a short time is much easier if you concentrate on Malcolm Gladwell's three classes of people than on "single mothers, the white rural poor, veterans, out-of-work Americans over 50". Also the opposite holds: If these groups want to stop the "misfortune of being insufficiently interesting" they really should think how they can become a member of the three groups.

Ha! I've used the exact same characterization.

Anyway, this seems entirely predictable given a couple different considerations.

1) People are most apt to effectively solve problems for which they have some context.

2) People who are underserved or otherwise have "real problems" don't often have much overlap with people who have discretionary money to pay for solutions.

So you end up with a selection bias in the pool of problem solvers (and thus the scope of addressable problems) and a selection bias in the expectation for investment returns. Which results in the best minds of our generation crunching all manner of machine learning models to make it so we don't have to do something mundane like selecting an outfit to wear that's color and style coordinated.

Perhaps universal basic income or other stimulus programs that will put dollars in the hands of those people, and then companies can compete for those dollars.