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Ranking YC W22 companies with a neural net

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Re: Ranking YC W22 companies with a neural net

#11

Post by bko »

lumost wrote: post #10
bko wrote: post #5

I checked the top 6 and 5 of them appear to be vaporware without a real product. To be fair, they're working on hard products, but I wonder if the model is selecting for that. According to the announcement[0] 29% of the batch were accepted with only an idea, so I guess that's not surprising. Slightly off topic, but I thought it was interesting that 29% of the batch has just an idea while 10% had more than $50k of mon…

Investors get yield by investing in unique products. When there is enough* money floating around, anything that can be built in a few weeks will have been built in a few weeks. This means that in order to get return, investors have to be willing to invest in longer term efforts which are sufficiently difficult to execute that other investors will either refuse to fund the project - or their teams will fail to deliver…

From my experience the two biggest pitfalls to startups are lack of product market fit and execution.

Product market fit is the hardest thing you have to do. Sometimes the current products are good enough or no one really wants the product. You also have to consider whether its economical. For instance, there may be a demand for flying cars but its uneconomical to driving, so it doesn't really work as a startup.

The other problem is execution. Take the high to medium end electric car market that sprung up after Tesla proved product market fit. There are probably a dozen electric car manufacturers that don't actually manufacture electric cars for sale. That's because its actually very hard to do that. It's easy to create a pitch deck and even a prototype, but shipping cars is hard.

A start up with no product has not proven either of these. They don't have product market fit because their idea has not been tested in the market yet. And they certainly didn't execute yet.

Compare that to a company with 600k+ a year revenue. They may not have proven the economics aspect (they're probably losing money) and they may never prove it. But they have proven that someone will pay for their product and they can create and deliver a product (assuming revenue isn't pre-sales). So if you were to compare to a few guys with an idea and a company shipping real product, I would think the one shipping is a lot further along and deserves a higher valuation in general

Re: Ranking YC W22 companies with a neural net

#12

Post by ericjang »

jedwhite wrote: post #9

Nice work with the write up and thank you for sharing this. The post is interesting, but I think the problem with the YCRank approach currently is that the labelling appears to be subjective opinion, at least if I understand correctly. Based on the post, you've trained the classifier by labelling a couple of examples of company descriptions you liked better than each other, based on subjective assessments like "harde…

> Based on that, this is essentially what you could call a "DudeRank Classifier" because as The Dude in the Big Lebowski says, "Yeah, well, that's just like, your opinion, man" :)

Yes, but isn't human VC investing already just a big DudeRank classifier?

Re: Ranking YC W22 companies with a neural net

#13

Post by yowlingcat »

ericjang wrote: post #12
jedwhite wrote: post #9

Nice work with the write up and thank you for sharing this. The post is interesting, but I think the problem with the YCRank approach currently is that the labelling appears to be subjective opinion, at least if I understand correctly. Based on the post, you've trained the classifier by labelling a couple of examples of company descriptions you liked better than each other, based on subjective assessments like "harde…

> Based on that, this is essentially what you could call a "DudeRank Classifier" because as The Dude in the Big Lebowski says, "Yeah, well, that's just like, your opinion, man" :) Yes, but isn't human VC investing already just a big DudeRank classifier?

Yes, in the same way that institutional investing and society is one big "DudeRank" filter. It doesn't mean that there's no structure, it means that you're not looking at the right place.

The difference between a layman investing and a skilled top 10% VC investing is that the latter already stands in very strong position of human and social capital networks, and moreover (if they're actually skilled) has experience materializing that capital into strong 0-1 outcomes. Or being really good at survivor's bias.

Re: Ranking YC W22 companies with a neural net

#14

Post by omarhaneef »

Here is an idea:

run it on old batches and see if you can predict which ones went on to raise a Series A, B, C or liquidity event.

Note: you might have to adjust for year since a Seed round in 2020 looks like a Series A in 2010.

Edit: I realize you have to figure out which companies raised out of 100s. As always, the data curation is harder than the algo. Not sure if techcrunch allows scraping.

Re: Ranking YC W22 companies with a neural net

#15

Post by lumost »

bko wrote: post #11
lumost wrote: post #10

Earlier quoted context omitted.

Investors get yield by investing in unique products. When there is enough* money floating around, anything that can be built in a few weeks will have been built in a few weeks. This means that in order to get return, investors have to be willing to invest in longer term efforts which are sufficiently difficult to execute that other investors will either refuse to fund the project - or their teams will fail to deliver…

From my experience the two biggest pitfalls to startups are lack of product market fit and execution. Product market fit is the hardest thing you have to do. Sometimes the current products are good enough or no one really wants the product. You also have to consider whether its economical. For instance, there may be a demand for flying cars but its uneconomical to driving, so it doesn't really work as a startup. The…

Here is where TAM starts to be a consideration. A company building 3D printed rocket engines can claim that the market is whatever size they want as they are not beholden to prove it for 5+ years. A company making a mobile blood tester isn't expected to have a working demonstration for 5+ years.

Compare this to a company with 600k in revenue, if they are in a competitive/small market - then this may mean a company which ultimately produces 100 million in revenue with 10-20 million in profit. Whereas the above two companies can pitch that they will eventually produce $BigNumber revenue with $HighProfit margins.

This scheme really only works when there is effectively infinite money floating around, and the opportunity cost of parking the money in a bad idea is low. I wouldn't expect a MagicLeap or Theranos to occur in any other environment.

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