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Can someone explain the dynamic that’s going on here? I somehow can believe that VCs are so stupid to be purely hype driven, so what is pushing them to make thi

by xevb3k 8y ago

Can someone explain the dynamic that’s going on here? I somehow can believe that VCs are so stupid to be purely hype driven, so what is pushing them to make this their investment focus? Do they just jump on every bandwagon in the hope that one of them might pan out?

manigandham 8y ago
Yes, due diligence is rare. This is portfolio theory in action, investing in many losing bets because the 1% will win big and create the returns. Disqualify the easy "no", then everything else is a "maybe". Invest and move on.
deleted 8y ago
[deleted]
azernik 8y ago
They put a lot of study into hyped categories, so they at least understand the businesses they're investing in. They can evaluate the caliber of talent, understand the depth of your moat, etc. Whereas with businesses outside of the hype (or outside of their specific specialty) they're not confident in their ability to even evaluate the potential of an investment. (I'm working on a startup right now that's in a similar position - non-hyped B2B segment. VCs are all "hey, look, we don't understand X market very well, maybe go to Y VC?" And Y VC is all "hmm, you're only tangentially related to Z market that we actually understand, so you'll need to get to Series A-sized revenues before we'll put in a seed-size investment.")
joewee 8y ago
VCs raise money the same way startups do, only with more regulation. For that bucket of money they likely presented a thesis for how they would used to invest in the ML/AI wave. Investors have rights if the money isn’t used they way they were told it would be. VCs raise money from fund managers (sovereign wealth funds for example) who also present a thesis to their stakeholders for how the money will be managed. Depends on the timing but I think VC focus on ML is a good indicator of where smart money thinks the money is going to be made. But there are a lot of people that catch a trend in the tail in, I don’t think we’ve gotten anywhere close to that for ML investments.
- gnicholas 8y ago
  You make a good point about a VC's responsibility to stick to the plan they sold their LPs on. This could explain a lot of group-think and myopia in the industry. But in this particular case, it was a corporate VC, investing their own money.
  - snaily 8y ago
    For corporate VCs, s/LPs/board/
  - y_molodtsov 8y ago
    That's even worse for you case, I suppose. In case of a usual VC funds it's about the returns, but corporate VCs also look for some potential synergy. They probably were looking for some ML startups that they could utilize later.
robryan 8y ago
The VCs is this case were probably looking for some tech that had a higher barrier to entry. Whether it is true or not in this case they may have perceived that if it was based on a ML approach it might be harder for the competition to replicate if it was successful.
wellboy 8y ago
VCs are very stupid most of the time. They don't understand even how ML works orh blockchain or any new trend. But their manager told them ML, ML, so they need to invest in ML. This is not a joke.
rorykoehler 8y ago
They invest in future potential. What you can do now is nearly irrelevant besides showing vision and competency. The next big thing isn't going to come from a couple of clever sql scripts.
- collyw 8y ago
  Neither will the next big failure. What has AI really given us so far? Amazon / Youtube recommendations and some self driving cars that have crashed in unexpected ways. (Have they managed to debug that yet?)
  - rorykoehler 8y ago
    From a VC perspective failure has a maximum downside of whatever you invested. Success has a limitless upside. That's why 9 out of 10 companies failing isn't an issue.
collyw 8y ago
Its not just every VC, its a good percentage,maybe the majority of developers out there.
Avshalom 8y ago
The thing is we're at a stage where companies hoover up every bit and byte of data they can get hold of on every customer and many non-customers because... well it's gotta be valuable some how right? But it's so much damn data so loosely coupled to a company's actual product that trying to get any actionable intelligence out of it is basically impossible. Enter ML. "Just" chuck your data at a NN use A/B tests to train and hope the company ends up with higher revenues, if they do: claim the 'data scientists' are definitely a profit center; if they don't: claim you need more data. Until Exxon-Mobile stops storing metrics on how many goldfish I own in a given month, cranking out ML related companies seems like a good bet for VCs because it's not going to get any easier to turn progressively more esoteric data points into money.

by xevb3k 8y ago

manigandham 8y ago

Yes, due diligence is rare. This is portfolio theory in action, investing in many losing bets because the 1% will win big and create the returns. Disqualify the easy "no", then everything else is a "maybe". Invest and move on.

deleted 8y ago

[deleted]

azernik 8y ago

They put a lot of study into hyped categories, so they at least understand the businesses they're investing in. They can evaluate the caliber of talent, understand the depth of your moat, etc. Whereas with businesses outside of the hype (or outside of their specific specialty) they're not confident in their ability to even evaluate the potential of an investment. (I'm working on a startup right now that's in a similar position - non-hyped B2B segment. VCs are all "hey, look, we don't understand X market very well, maybe go to Y VC?" And Y VC is all "hmm, you're only tangentially related to Z market that we actually understand, so you'll need to get to Series A-sized revenues before we'll put in a seed-size investment.")

joewee 8y ago

VCs raise money the same way startups do, only with more regulation. For that bucket of money they likely presented a thesis for how they would used to invest in the ML/AI wave. Investors have rights if the money isn’t used they way they were told it would be. VCs raise money from fund managers (sovereign wealth funds for example) who also present a thesis to their stakeholders for how the money will be managed. Depends on the timing but I think VC focus on ML is a good indicator of where smart money thinks the money is going to be made. But there are a lot of people that catch a trend in the tail in, I don’t think we’ve gotten anywhere close to that for ML investments.

gnicholas 8y ago
You make a good point about a VC's responsibility to stick to the plan they sold their LPs on. This could explain a lot of group-think and myopia in the industry. But in this particular case, it was a corporate VC, investing their own money.
- snaily 8y ago
  For corporate VCs, s/LPs/board/
- y_molodtsov 8y ago
  That's even worse for you case, I suppose. In case of a usual VC funds it's about the returns, but corporate VCs also look for some potential synergy. They probably were looking for some ML startups that they could utilize later.

robryan 8y ago

The VCs is this case were probably looking for some tech that had a higher barrier to entry. Whether it is true or not in this case they may have perceived that if it was based on a ML approach it might be harder for the competition to replicate if it was successful.

wellboy 8y ago

VCs are very stupid most of the time. They don't understand even how ML works orh blockchain or any new trend. But their manager told them ML, ML, so they need to invest in ML. This is not a joke.

rorykoehler 8y ago

They invest in future potential. What you can do now is nearly irrelevant besides showing vision and competency. The next big thing isn't going to come from a couple of clever sql scripts.

collyw 8y ago
Neither will the next big failure. What has AI really given us so far? Amazon / Youtube recommendations and some self driving cars that have crashed in unexpected ways. (Have they managed to debug that yet?)
- rorykoehler 8y ago
  From a VC perspective failure has a maximum downside of whatever you invested. Success has a limitless upside. That's why 9 out of 10 companies failing isn't an issue.

collyw 8y ago

Its not just every VC, its a good percentage,maybe the majority of developers out there.

Avshalom 8y ago

The thing is we're at a stage where companies hoover up every bit and byte of data they can get hold of on every customer and many non-customers because... well it's gotta be valuable some how right? But it's so much damn data so loosely coupled to a company's actual product that trying to get any actionable intelligence out of it is basically impossible. Enter ML. "Just" chuck your data at a NN use A/B tests to train and hope the company ends up with higher revenues, if they do: claim the 'data scientists' are definitely a profit center; if they don't: claim you need more data. Until Exxon-Mobile stops storing metrics on how many goldfish I own in a given month, cranking out ML related companies seems like a good bet for VCs because it's not going to get any easier to turn progressively more esoteric data points into money.