

Is data going to eat venture capital? Why we hired a data scientist - jpwise
http://www.jameswise.ghost.io/will-data-eat-us/

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mistermcgruff
I like to think about whether a problem is inherently antagonistic when
deciding whether data/models/etc. can automate it well ("eat it" in the words
of the article).

Not antagonistic -- predicting whether someone will get lung cancer...they're
probably not going to falsify the data from their physical, etc.

Antagonistic -- detecting network intrusions or predicting phishing attacks.
These folks fight back, i.e. disguise, adapt, hide

In the latter case, a human component to a decision task (detection, labeling,
etc.) will always remain.

So then when it comes to investment decisions, let's say data plus supervised
ML becomes the name of the game. Start-ups will learn the model features of a
business that gets investment dollars. They'll design themselves specifically
to those features and in a sense dupe models for a while until the models are
adapted. In this way, the human component can't be left out.

Of course, it doesn't take a model to get duped. A lot of companies might hire
a bunch of phds and data scientists right before being acquired, because each
hire bumps up the value of the company, etc. These moves are taken to increase
a valuation rather than actual value and that's antagonistic in much the same
way hiding spam is.

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fhuszar
Yes, startups try to game VCs already. But if the 'features' that your
business needs to achieve for VC are 1) hard to achieve and 2) generally drive
you towards success then what's the issue with that? We want more startups to
succeed and VC investment is an important feedback mechanism that ought to
drive more startups towards success or early discovery of failure.

I mean let's say startups try to game the VC game and they learn they need to
demonstrate product market fit before seeking investment. Is this a bad thing?
We'll try to be as transparent about our insights as possible so startups know
what we think drives/indicates their success the best.

I think you are right in that final decision is certainly not going to be
reduced to a supervised ML algorithm classifying investment opportunities. But
I don't think this fundamentally has to do anything with antagonistic and non-
antagonistic - you just need different types of methods

