This might sound a bit cynical but it seems we are at a stage where using the words - data, ML or AI seems enough.
The post while being vague constantly throws around the word - "data". What data really? If the Anti Portfolio discussion from yesterday (https://news.ycombinator.com/item?id=15547136) is any indication, it is difficult to predict growth - data or not. For example, there already was Friendster. What data will help in deciding whether to invest in early stage Facebook? Or what data will help in decide investing in Uber? Today's startup are about growing, engagement and then possibly trying to make money. The truth remains even the best will fail at the last step.
1. Good companies won't pitch you, it's common for VCs to reach out to the top companies while they are not raising funding. So good luck running your analysis on data you don't have.
2. There are no metrics that identify a good from a bad investment. Maybe this would work in some cases for later stage investments. But even at the later stage the metrics are all over the place.
3. Companies applying to this VC fund will game the system
4. This is not a new thing, every VC firm looks at your metrics.
1. Good and great companies actively seek out funding, especially at early stages. Lots of good and great ones slowly build and get contacted, too.
2. There certainly are good metrics, otherwise the basic tenets of business that have evolved over the past 5,000 years would exist. There may not be metrics that say if a certain product/service will be a smash hit or not, but it did not take a crystal ball to see that Pets.com was incredibly unsustainable, or that Groupon (and Blue Apron was too easy to replicate and thus overvalued, or that Juicero was an absolutely asinine idea destined for failure (massively overengineered squeezer that wasn't even necessary for their overpriced juice bags).
3. People game every VC system. Remember Pixelon? Theranos was WIDELY derided in medical and medical research circles from the get go. No way a 19 year old drop out came up with such a massive breakthrough in blood tests, she didn't even have the time to learn the medical basics. Faraday has looked sketchy from the outset as well.
4. This statement I wholly agree with, and it happens to back up my disagreement with #2.
The flaw here is using the wrong metrics. Once a company has found a repeatable and growing business model, it’s trivial to raise money and there’s no reason to pick Social Capital over anyone else.
The big market opportunity is to fund startups at the very earliest signs of success. When all they have to show is some code and a few Hacker News upvotes or GitHub stars.
Someone is going to make YCs returns look weak by funding this early. And nothing would do more for diversity than a low barrier test that is 100% blind and meritocratic.
From my experience, most of it is about connections. I have seen $41 MM thrown at startups like Color and them failing a month later! Why just recently, FileCoin raised tens of millions from VCs, before goong on to raise $200 MM from crypto holders around the world for a product that hasn't even been developed, in a space where MaidSafe and IPFS etc. already exist. How? Because it was touted by CoinList, started by among others AngelList founder Naval Ravikant who has all the connections.
Filecoin is made by the people who made IPFS, and it is an incentivization system for IPFS. Maidsafe has been around for more than 10 years and has yet to launch, and it’s unclear what exactly it does and if it even competes with Filecoin at all.
Filecoin vc deal was the best imho executed vc play I have seen in a while. for 50m with a discount they were able to sell approx 200m to coin buyers. This not only offset their risk but literally gave the x4 in raw cash infusion in 2 weeks, growing the valuation without any time and this was done all on vc reputation/ name recognition/ and hype building. Very well done...
Social metrics. Would make the Social Capital name genuinely meaningful :-)
The “social response” (HN, GitHub, reddit, ...) to a new project, product, or service is all you should need to predict success at a high enough rate to do extremely well.
It’s not a matter of difficulty so much as conviction and intelligence.
It's hard to know what they are actually doing since the blog post doesn't explain what Capital-as-a-service is. But, if this works for them, I bet it is because it increases their deal flow. With this system and their marketing of it they can get more founders to submit company data to them and see companies that are outside their network. It would be much easier to filter through a bunch of business data submitted via a web form rather than going through pitchdecks that founders e-mail them.
Flagged because this says nothing and is just blah blah blah endless advertising copy. Probably bought half those upvotes, or do you HNers upvote without reading the article?
Genius. Finally, someone it taking a "moneyball" approach to VC. The critical comments on this thread have missed the point.
Although the author is vague, it sounds like they built a system to analyze company metrics and thus screen investments at scale. This enables them to see more investments around the world, and makes it easier for entrepreneurs to "pitch" them, because they don't need to do the time consuming, typical Silicon Valley pitch process. Many more companies can get funded this way. And those investments will be less competitive and garner lower valuations (implying better returns). Again... genius.
They built a system that does this at scale: 1) collects and organizes the right data and spits out analysis -- this can be painful from the many times I've done it. Investment banks hire armies of young analysts and pay them six figures to do this at scale 2) made it self-serve for entrepreneurs seeking funding -- not having to do the CRM/pipeline mgmt process associated with private investing is a massive efficiency improvement. VCs hire associates and partners and pay them handsomely to do this at small scale (hundreds of deals seen / year / professional)
As someone who has done a lot of private investing, the value here is obvious and substantial for the investors and the entrepreneurs.
Not sure it's really all that original, except for possibly the "scale" part. A lot of VCs (Correlation must notably jump to mind) have a multi-variate model which they run, and then ultimately decide with standard terms within about 24 hours.
I submit the central problem is that a lot of things don't scale smoothly, and figuring out what doesn't scale costs money. Discovering how to transition from 10 to 100 is easy. 100 to 10,000 might be a 9 figure problem, but everything after 10,000 is vertical growth until you hit 10M.
The other issue is that some great ideas need $10M or $100M or $1B up front. Google's TPUs are probably a 9-10 figure investment, with zero dollars income until the whole system is deployed at scale. But it becomes a money printing machine after that.
Readit News
The post while being vague constantly throws around the word - "data". What data really? If the Anti Portfolio discussion from yesterday (https://news.ycombinator.com/item?id=15547136) is any indication, it is difficult to predict growth - data or not. For example, there already was Friendster. What data will help in deciding whether to invest in early stage Facebook? Or what data will help in decide investing in Uber? Today's startup are about growing, engagement and then possibly trying to make money. The truth remains even the best will fail at the last step.
IndieVC[0] is the only really interesting spin on investing I've seen out there and they open-sourced their terms sheet publicly at launch.
[0] http://www.indie.vc/
2. There are no metrics that identify a good from a bad investment. Maybe this would work in some cases for later stage investments. But even at the later stage the metrics are all over the place.
3. Companies applying to this VC fund will game the system
4. This is not a new thing, every VC firm looks at your metrics.
2. There certainly are good metrics, otherwise the basic tenets of business that have evolved over the past 5,000 years would exist. There may not be metrics that say if a certain product/service will be a smash hit or not, but it did not take a crystal ball to see that Pets.com was incredibly unsustainable, or that Groupon (and Blue Apron was too easy to replicate and thus overvalued, or that Juicero was an absolutely asinine idea destined for failure (massively overengineered squeezer that wasn't even necessary for their overpriced juice bags).
3. People game every VC system. Remember Pixelon? Theranos was WIDELY derided in medical and medical research circles from the get go. No way a 19 year old drop out came up with such a massive breakthrough in blood tests, she didn't even have the time to learn the medical basics. Faraday has looked sketchy from the outset as well.
4. This statement I wholly agree with, and it happens to back up my disagreement with #2.
Amazon would have probably gone the way of pets had it not found AWS.
The big market opportunity is to fund startups at the very earliest signs of success. When all they have to show is some code and a few Hacker News upvotes or GitHub stars.
Someone is going to make YCs returns look weak by funding this early. And nothing would do more for diversity than a low barrier test that is 100% blind and meritocratic.
Perhaps, but social capital may require giving away less of the farm to do it.
https://medium.com/swlh/diligence-at-social-capital-part-1-a...
https://medium.com/swlh/diligence-at-social-capital-part-2-a...
https://medium.com/@jonathanhsu/diligence-at-social-capital-...
https://medium.com/@jonathanhsu/diligence-at-social-capital-...
https://medium.com/@jonathanhsu/diligence-at-social-capital-...
https://medium.com/swlh/diligence-at-social-capital-epilogue...
The “social response” (HN, GitHub, reddit, ...) to a new project, product, or service is all you should need to predict success at a high enough rate to do extremely well.
It’s not a matter of difficulty so much as conviction and intelligence.
Although the author is vague, it sounds like they built a system to analyze company metrics and thus screen investments at scale. This enables them to see more investments around the world, and makes it easier for entrepreneurs to "pitch" them, because they don't need to do the time consuming, typical Silicon Valley pitch process. Many more companies can get funded this way. And those investments will be less competitive and garner lower valuations (implying better returns). Again... genius.
Wait, so they invented... financial analysis? KPIs? BI?
Really?
They built a system that does this at scale: 1) collects and organizes the right data and spits out analysis -- this can be painful from the many times I've done it. Investment banks hire armies of young analysts and pay them six figures to do this at scale 2) made it self-serve for entrepreneurs seeking funding -- not having to do the CRM/pipeline mgmt process associated with private investing is a massive efficiency improvement. VCs hire associates and partners and pay them handsomely to do this at small scale (hundreds of deals seen / year / professional)
As someone who has done a lot of private investing, the value here is obvious and substantial for the investors and the entrepreneurs.
The other issue is that some great ideas need $10M or $100M or $1B up front. Google's TPUs are probably a 9-10 figure investment, with zero dollars income until the whole system is deployed at scale. But it becomes a money printing machine after that.