Distilled: Dharmesh Shah on Bitclout for LinkedIn
Disclaimer: These are a set of notes I took from listening to Dharmesh Shah. They don't fully represent his thoughts as some of these might be rephrased to something I understand more clearly.
LinkedIn was great for its day but now we have a new set of needs that can be addressed with technologies that weren't available before.
Separating the noise and the signal in the platform is very hard. Over time LinkedIn has diminishing utility, where it just becomes hateful.
Imagine if you take what BitClout did for Twitter in the blockchain but in LinkedIn. So you have your own kind of currency. Given that, there would be situations where an engineer could offer to do a certain job for $500 they would look at the company apply for it and do a relatively good job at it. Or for $10 I'll open your email or for $X all to Y.
In BitClout everything has got a price, every coin has its own value. Right now when you go on LinkedIn, the hierarchies are not clear, meaning that people that are either recruiters or people that are at the bottom of the career ladder are connecting and reaching out to the people that are at the top of the career ladder while the latter not having a rear incentive to join and end up ignoring the site. No one wants to get inundated with crap they don't really want.
Because in BitClout everyone has a coin value, you have backed in a career worth and career value that that person is bringing to the table into the network. So then you would have:
- A: Ways to buy their attention by buying their coin, which in return increases their price which is of mutual beneficialness. As opposed to right now that you pay LinkedIn to spam people, and the spammed person doesn't perceive any of the revenue.
- B: Once someone reaches out you can quickly assess their value in the network based on how much other people believe in this person. That's because every node wouldn't look the same on the surface.
Another major innovation is that you could use a page rank for the professional graph (like Google does for SEO). Let's introduce what page rank is first:
The original idea behind Google, and what made it the powerhouse it is today, is that to determine what the best set of results are, they look for mainly 2 things:
- The overall content of the page (that the result matches the intent of the query).
- Authority. The way they assess this is by checking the number of links that are coming into one specific page and the value of each individual link (which depends on the authority - page rank - of those pages). Another important factor is that the amount of pagerank that you pass across the links from your page is proportional to your own pagerank. This means that it gets "distributed" across the links that you reference to.
Right now, LinkedIn offers a symmetrical graph where each person you follow gets an equal number of "follow value" - which is 1 because it isn't weighted in any way-. If you take the pagerank idea from Google and apply it to a professional network graph, then when you endorse someone whilst having a certain authority yourself (currency even if you include your coin), then as a result of that the endorsee's currency price goes up.
So when you really like one person you can buy their coin. Meaning that instead of just following a person, which is a pretty light signal because you can do it infinitely, you could own some of that person's coin (strong endorsement), and because a person with a valuable currency holds a coin of another, that goes up by the price of the coin you paid + a multiple because of pagerank.
This is an idea of a social network that is inverted in the sense that instead of one platform that owns all the data, there's no company where all the data is free and you own all the business on top of it.