
Google Ventures uses algorithms to approve or kill VC investments - kjw
https://www.axios.com/newsletters/axios-pro-rata-52e865b9-2169-4fae-821e-a84a0e98eaa0.html
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aaavl2821
> Inputs into "The Machine" include round size, syndicate partners, past
> investors, industry sector and the delta between prior valuation and current
> valuation. The algorithm then ranks deals on a 10-point scale, with green
> said to represent 8 or above

I'm sure there are more inputs than this, but from that list you'd imagine
they basically pick deals based on who else is investing. which is not that
different from many other VCs. at least in biotech, i typically see GV co-
investing in deals led by other top notch VCs

~~~
_hyn3
> they basically pick deals based on who else is investing.

Another phrase for that is "herd" mentality, which will hopefully result in
de-risked "safe" investing, but VC's are supposed to be looking for true
breakout possibilities. If LP's wanted safe, they'd buy Treasury notes.

~~~
writepub
There are huge gains to be had in herd-ing. For instance, if everyone herds
towards Uber instead of Lyft, you effectively pick Uber as the winner by
merely herding, instead of merit or market economics

~~~
taurath
And in that case, the worst possible outcome is to have many competing funded
companies in the same space. Merit doesn’t enter into it much.

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oldgradstudent
That would explain their Juicero investment.

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droopyEyelids
If a significant chunk of VCs invest based on an algorithm following the
investments of other VCs running the same algo... positive feedback loop.
Juicebox squeezer receives $120m investment.

~~~
oldgradstudent
Supposedly, a lot of this was going on during the (previous) housing bubble.

[https://www.wired.com/2009/02/wp-quant/](https://www.wired.com/2009/02/wp-
quant/)

~~~
taurath
And right now companies are absurdly cash rich and the hardest problem is
where to invest the money.

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maxxxxx
Isn't that what a lot of Excel spreadsheets do? I think investments and
acquisitions have used algorithms for a long time.

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JumpCrisscross
By 2014, "checklists" and "investment criteria" had rebranded themselves as
"algorithms" across most of finance.

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pkaye
And by 2018 they have been re-branded as "machine learning algorithms."

~~~
visarga
Just a fancy formula that works based on people generated input. Without
people input said formula would be useless.

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jamez1
This is nothing new, most funds treat portfolio construction as an
optimization problem, where the objective function is some risk over return
metric.

"The Machine" is an optimizer, all they've done is build one that looks at
early stage companies.

~~~
visarga
G also has inside access to a lot of information from search and email. Could
be using that to optimise their portfolio.

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inputcoffee
The article has uncovered sources that claim the algorithm makes the ultimate
decision and other sources that claim it doesn’t.

If it _does_ make the ultimate decision and not just for political reasons
then this is very interesting. Having input data that is sufficiently
informative is important on a number of levels. Firstly this means that it is
possible to pick winners on the basis of other VCs etc Secondly it means one
doesn’t have to be personally concerned with the story if others can vet it
for you.

If the machine doesn’t make the ultimate decision then — as others point out —
it’s just old fashioned screens and checklists with a new interface.

~~~
aaavl2821
If the main input is quality of other VCs, then at some point a VC has to
decide to invest based on fundamentals rather than what other investors are
doing. a group of "fundamental" investors with good track records then would
dictate what the rest of the market invests in.

you sort of see this dynamic play out in reality. YC is an example: they
invest early, before other investors often, so they cant rely on other
investors as a signal. they've done well though, so many investors follow
them. there are more follow-on investors than successful "fundamental"
investors, so there's often a valuation step up when follow on investors join
that benefits the fundamental investors

same thing plays out in biotech. theres been a massive influx of capital into
biotech VC, but not a big increase in the number of funded startups. most
startups that go on to raise money are seeded in house by a handful of VCs.
these VCs then fund the series a. they get big step-ups for series b and
beyond deals and capture nice returns

im working on a more rigorous analysis to understand whether these anecdata
are true in reality

~~~
inputcoffee
I agree with your description of he dynamic at play. It raises two questions:

1\. Is the money that the startup attracts responsible for its success? In
other words if a mediocre company goes through Y Combinator and then attracts
a $55 million round, is it more likely to succeed than a great company that
does not? (Let’s day the mediocre company doesn’t squander the cash wastefully
but slowly looks for the product market fit)

2\. Are there fundamentals that can be distinguished from an “observer
effect.” Suppose everyone believes that a company coming out of Stanford is
more likely to succeed than one coming out of (say) Babson. Does believing it
make it true because the company attracts more money in each round?

These two thoughts are variations on a theme of the role of signaling in
picking out fundamentals.

Edit: I should also point out that GV might also use “true” fundamentals like
search results, trends, etc

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ErikAugust
I wonder if the prospective company's G Suite data gets crunched.

~~~
jasonvorhe
And risk losing thousands of existing paying customers if it ever came out?

Not a smart choice, considering you're betting on the company you're illegally
spying on to get profitable at some point.

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kuschku
It’s not illegal if it’s in the ToS.

And even the data of enterprise customers is used to improve Google’s machine
learning algorithms, according to those.

So not illegal, just entirely immoral. But when has that ever stopped Google?

~~~
microdrum
Exactly. These days this is exactly how Google works. Ask an AdWords,
Analytics, or AdSense user who slowly realizes that Google has full access to
their business data for any purpose.

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monkeydust
Anyone know of open datasets around early stage startups and success? Guess
might be something mashing up crunchbase, angel.co....

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aaavl2821
there is very little public data available on startups as compared to private
companies. so id imagine for an algorithm to be useful there would have to be
a lot of proprietary data. further, id imagine a lot of this data is somewhat
subjective -- ratings of management team, market potential (when a market is
still not defined enough to quantify), etc. so its possible that many of the
quantitative inputs have some degree of subjectivity -- the human element is
still very present, its just hiding behind data

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baccheion
Best-performing seed round investors:

    
    
         7.5724 . . . . True Ventures 
    
         6.6294 . . . . Accel Partners
         6.1189 . . . . Seedcamp 
    
         4.7562 . . . . Nxtp Labs
         4.7474 . . . . Y Combinator
         4.4846 . . . . Softtech Vc
         4.4720 . . . . Gecad Group 2
         4.4720 . . . . Radu Georgescu
         4.4246 . . . . Creandum
         4.3653 . . . . Sv Angel
         4.3636 . . . . Google Ventures
         4.3016 . . . . Greylock
    
         3.9381 . . . . Baseline Ventures
         3.8225 . . . . First Round Capital
         3.7816 . . . . Mitch Kapor
         3.6702 . . . . Atomico
         3.5938 . . . . Felicis Ventures
         3.3963 . . . . Freestyle Capital
         3.2857 . . . . Yee Lee
         3.2183 . . . . Naval Ravikant
         3.1193 . . . . Boldstart Ventures
         3.1038 . . . . Keadyn
         3.1007 . . . . Birchmere Ventures
         3.0376 . . . . Betaworks
         3.0012 . . . . Hyde Park Angels 
    
         2.8730 . . . . Ff Angel Llc
         2.8578 . . . . Slow Ventures
         2.8268 . . . . Rose Tech Ventures
         2.8052 . . . . Chicago Ventures
         2.7709 . . . . I5Invest
         2.6940 . . . . K9 Ventures
         2.6469 . . . . Founders Co Op
         2.6132 . . . . Oleg Tscheltzoff
         2.5637 . . . . Nyc Seed
         2.5558 . . . . Ta Venture
         2.5148 . . . . Geoff Ralston
         2.4748 . . . . Cnm Ventures
         2.4327 . . . . Genacast Ventures
         2.3829 . . . . Wi Harper Group
         2.3526 . . . . Allen Morgan
         2.3171 . . . . W Media Ventures
         2.2827 . . . . Oliver Jung
         2.2532 . . . . Amplify La
         2.2509 . . . . Chris Devore
         2.2481 . . . . Dharmesh Shah
         2.2291 . . . . Innospring
         2.2290 . . . . The Accelerator Group
         2.1947 . . . . Gary Vaynerchuk
         2.1791 . . . . Crunchfund
         2.1772 . . . . Jon Callaghan
         2.1700 . . . . Oca Ventures
         2.1666 . . . . Kfw
         2.1652 . . . . Plataforma Capital Partners
         2.1136 . . . . High Tech Gruenderfonds
         2.1079 . . . . Marc Simoncini
         2.0933 . . . . Tom Mcinerney
         2.0908 . . . . Steve Anderson
         2.0879 . . . . Battery Ventures
         2.0590 . . . . New York Venture Partners
         2.0256 . . . . Emerge
         2.0038 . . . . Andy Appelbaum
    

Worst-performing seed round investors:

    
    
        -6.9802 . . . . Wayra
    
        -5.8839 . . . . Start Up Chile
        -5.0805 . . . . Startupbootcamp
    
        -4.0593 . . . . Jumpstartinc
    
        -3.8784 . . . . Sosventures
    
        -2.7610 . . . . Start Engine
        -2.6717 . . . . Social Starts
        -2.5748 . . . . Ff Venture Capital
        -2.5641 . . . . Ben Franklin Technology Partners Of Southeastern Pennsylvania
        -2.5460 . . . . Ace And Company
        -2.4309 . . . . Quest Venture Partners
        -2.0690 . . . . Masschallenge
    

Best-performing series A round investors:

    
    
         7.0711 . . . . Greycroft Partners
    
         5.5854 . . . . Venrock
         5.4365 . . . . Trinity Ventures
    
         4.9683 . . . . Intel Capital
         4.9600 . . . . Scott Banister
         4.9400 . . . . Kleiner Perkins Caufield Byers
         4.8362 . . . . Redpoint Ventures
         4.6803 . . . . Ron Conway
         4.5240 . . . . Sv Angel
         4.3989 . . . . Crosslink Capital
         4.3097 . . . . Storm Ventures
         4.0204 . . . . Shasta Ventures
    
         3.9124 . . . . E Ventures
         3.6809 . . . . Leapfrog Ventures
         3.5279 . . . . Khosla Ventures
         3.3344 . . . . Valor Capital
         3.2867 . . . . Accel Partners
         3.2164 . . . . Austin Ventures
         3.1917 . . . . Accelerator Ventures
         3.1199 . . . . Inveready Technology Investment Group
         3.0892 . . . . Signia Venture Partners
         3.0283 . . . . Mangrove Capital Partners
    
         2.9607 . . . . Alliance Of Angels
         2.9039 . . . . Ventures West
         2.8911 . . . . Novartis Venture Fund
         2.8627 . . . . Carmel Ventures
         2.8622 . . . . Brightspark Ventures
         2.8440 . . . . Omnes Capital
         2.8099 . . . . Reid Hoffman
         2.7943 . . . . Alta Partners
         2.7847 . . . . Partech International
         2.7432 . . . . Balderton Capital
         2.7391 . . . . Helion Venture Partners
         2.7341 . . . . Wellington Partners
         2.6948 . . . . Mercury Fund
         2.6859 . . . . Sofinnova Partners
         2.6841 . . . . Holtzbrinck Ventures
         2.6694 . . . . Metamorphic Ventures Llc
         2.6218 . . . . Frazier Healthcare Ventures
         2.5929 . . . . Doughty Hanson Technology Ventures
         2.5796 . . . . Bessemer Venture Partners
         2.5772 . . . . Menlo Ventures
         2.5608 . . . . Divergent Ventures
         2.5439 . . . . Jafco Asia
         2.5071 . . . . Vanedge
         2.4915 . . . . Amadeus Capital Partners
         2.4576 . . . . Texas Venture Labs
         2.4355 . . . . Newfund Management
         2.3769 . . . . August Capital
         2.3561 . . . . Dave Morin
         2.3195 . . . . Xange Private Equity
         2.3058 . . . . 3Ts Capital Partners
         2.3021 . . . . Hummer Winblad Venture Partners
         2.2973 . . . . Flybridge Capital
         2.2966 . . . . High Tech Gruenderfonds
         2.2782 . . . . 5Am Ventures
         2.2499 . . . . Constellation Ventures
         2.2463 . . . . Opus Capital
         2.2099 . . . . Softbank Capital
         2.1956 . . . . Miramar Venture Partners
         2.1705 . . . . Oregon Angel Fund
         2.1541 . . . . Greylock Partners Israel
         2.1507 . . . . Schroders Private Bank
         2.1277 . . . . Dcm
         2.1233 . . . . Alta Berkeley Venture Partners
         2.1211 . . . . Qualcomm
         2.0847 . . . . Mountain Partners
         2.0840 . . . . Boxgroup
         2.0605 . . . . Azure Capital Partners
         2.0559 . . . . Legend Capital
         2.0505 . . . . High Peaks Venture Partners
         2.0434 . . . . Ggv Capital
         2.0280 . . . . Kpg Ventures
         2.0204 . . . . David Tisch
         2.0045 . . . . Bluerun Ventures
    

Worst-performing series A round investors:

    
    
        -3.1130 . . . . Founders Fund
        -3.0555 . . . . Andreessen Horowitz
        -3.0272 . . . . Emergence Capital Partners
    
        -2.9966 . . . . Crp Companhia De Participacoes
        -2.6290 . . . . Techcolumbus 2
        -2.4801 . . . . Sigma Partners
        -2.4795 . . . . Nexus Venture Partners
        -2.4344 . . . . J Hunt Holdings
        -2.4001 . . . . Sevin Rosen Funds
        -2.3166 . . . . Commonangels
        -2.2516 . . . . Oca Ventures
        -2.2448 . . . . Long River Ventures
        -2.2120 . . . . Alloy Ventures
        -2.2035 . . . . Eden Ventures
        -2.1966 . . . . Granite Ventures
        -2.1778 . . . . Allegis Capital
        -2.1778 . . . . Shenzhen Capital Group
        -2.1708 . . . . Morgenthaler Ventures
        -2.1347 . . . . Golden Seeds
        -2.0325 . . . . Split Rock Partners

~~~
ptd
If you don’t mind sharing, where did you get this data?

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Tim_cas
You can't do this with crypto startups

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paulgb
Given how that market is doing, that might be to its credit.

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s73v3r_
Wow. They can't bother to have people working in customer service, and now
they can't even bother to have people working on where they invest their
money?

Please tell me that the execs are next to be automated.

~~~
ParanoidShroom
Oh cmon, like Ycombinator doesn't do the same. They will probably have a bunch
of datapoints an features to evaluate each batch. Be it a human or a machine,
it doens't matter. Only the accuracy.

