Flightcaster (YC S09) and the Future of Asymmetric Intelligence as a Product | Hacker News Reader

Flightcaster (YC S09) and the Future of Asymmetric Intelligence as a Product

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93 pointsjaf12duke8 comments

All of the points in this article can be applied to SeatGeek, as well.

Disclosure: I wrote the piece.

But yes, I agree. SeatGeek fits the model perfectly, in terms of applying analytics to publicly available data in search of non-obvious or otherwise difficult to intuit insights. Good mention.

i wish that there were a clearer way for people to profit from this kind of stuff. people generally won't buy information.

If you mean the general public with "people", like consumers that might be true. But there are a lot of companies willing to pay you truckloads of money for the right information.

Tell that to Bloomberg.

You ever used a Bloomberg?

It's not just that they have some data. It's that they have:

- strong completeness of data

- often they have data you cannot get elsewhere (bond trading data)

- there's a built-in messaging system that traders often think they need

- it is itself a development platform

I've been in the position of being a trader and considering data to purchase. Everything seems too expensive very quickly.

My trading desk had ONE terminal in the corner. And that's at $1500/year.

And that's for something as essential as a Bloomberg.

Do you see what I mean?

No argument here that the market for data is woefully underdeveloped. What we have now is a market in which there are very few visible sellers and fewer visible buyers, with extreme inefficiencies in connecting one to another and processing the transaction.

My hope and expectation, obviously, is that that will change.

I think Bloomberg is both an excellent and a poor example. On the one hand, @joshu is exactly right that it can be a difficult sell. On the other, they've clearly built a very sizable business complementing data with the additional pieces described.

More to the point, Bloomberg is - in my opinion - selling data with a.) very little value add and b.) a perceived transience in value. Not that this is necessarily bad, particularly when you're selling to a volume audience, but it is different from retailing specialized, derived datasets.

But we shall see.

both network and finance analytics are near the top for projected job growth. with inelastic graduation rates this means increased salaries. it makes sense that entrepreneurs that can come into this space and automate some of it will kill.

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