It seems like the way to make the most money from working in tech jobs would be to find identifying startups/companies that are likely to do well in the future, work with them, and make money from the equity you get. For example, Dustin Moskovitz suggests that you can get a better return from trying to be employee #100 at the next Facebook or Dropbox than by being an entrepreneur Any thoughts on how to identify startups/companies likely to do well/be valuable to work for, or at least rule out ones likely to fail? (It seems like the problem of doing this from an investor standpoint is well investigated, and hard to do, but the employee standpoint is different).
It seems like the correct approach would be to make predictions on the future performance of a bunch of startups and track the results, in order to calibrate your predictive model, but one would need time to build up a prediction history. Short of this, there might be heuristics that are sort of helpful, ie. I’d guess that startups with more funding or more employees are more likely to succeed due to more people having confidence in them and having survived for some period of time already, but this also indicate that you are likely to get less equity.
It strikes me as implausible that the best way to make money in technology is to try to make the most money right away. So I would go a step further back than you and look for good learning opportunities rather than ones which will make a lot of money.
To this end, I think a company’s degree of sponsorship is often underrated when people are making decisions. I wrote more about this here.
Startups are an extremely mixed bag when it comes to learning opportunities. You will be given a lot more responsibility than you would in a big company, but you also receive less mentoring and you will have to do a lot more grunt work that would be outsourced to lower skilled people in a big company. My guess is that the average big company is a better employer than the average startup for people who are earning to give.
To the extent you do want to join a profitable startup, I would guess that it’s very hard to outperform professional investors. So if they’ve recently raised funding it’s probably best to just assume that valuation is correct, but it could be tricky if they haven’t raised recently. If they haven’t raised recently because they’ve got a good cash flow, you could look at EBITDA or revenue multiples; if they haven’t raised recently because they can’t find investors then that’s probably a red flag.
To the extent you do want to join a profitable startup, I would guess that it’s very hard to outperform professional investors.
Although I’m a fan of this attitude in general, venture investment is not the ideal candidate for the efficient market hypothesis, and investors have very different deal structure from employees. Some notes:
VCs manage other people’s money, which means they’re basically buying options on the startups, not the startups themselves. As a result they do a lot of variance-chasing.
The market for venture investments is incredibly illiquid. It’s virtually impossible to short sell them, for instance, which inflates valuations.
The things that get traded in a venture deal are not just cash. A company that got valued at $10m by Sequoia is likely more valuable than one that got valued at $10m by a first-time investor. Similarly, a company that got valued at $10m in a round where the VC got a 3x liquidation preference is much less valuable than the equivalent with no liquidation preference.
Anecdotally, investors often do not know very much about the businesses they invest in and do not understand them well. My impression is that most venture investors are not much better than an index of startups, but mostly profit/stay in business because (a) the entire sector is growing and (b) they’re the ones with access to dealflow.
In summary, investor valuations are biased high, probably by a large factor IMO, and also have incredibly high variance. I would use them only with extreme caution.
Employee #100 seems a bit implausible. If you joined Dropbox as employee #100 it would be in early 2012, at which point they had just gotten a $4B valuation. It’s only gone up 2.5x since then—a mere 35% per year—so you probably wouldn’t have done better than a founder over the equivalent timespan. Especially once you take into account the many worse options that were in Dropbox’s reference class in 2012, like Fab.
That said, I agree that trying to forecast startups is probably a useful exercise—and maybe even possible to do historically, if you’re interested in ones as high-profile as Dropbox. It’s an open question to me how efficient the market is here (i.e., are companies with semi-obvious predictors of success likely to offer less equity).
I heard a rumor suggesting Dropbox was slower to hire than the typical tech company (i.e. a $4B company with <100 employees is somewhat atypical even in tech), though this may be what the norm is trending towards.
It’s only gone up 2.5x since then—a mere 35% per year—so you probably wouldn’t have done better than a founder over the equivalent timespan. Especially once you take into account the many worse options that were in Dropbox’s reference class in 2012, like Fab.
Great point. I would add that 35% annual raises are completely within the realm of possibility in direct employment as well.
It seems like the way to make the most money from working in tech jobs would be to find identifying startups/companies that are likely to do well in the future, work with them, and make money from the equity you get. For example, Dustin Moskovitz suggests that you can get a better return from trying to be employee #100 at the next Facebook or Dropbox than by being an entrepreneur Any thoughts on how to identify startups/companies likely to do well/be valuable to work for, or at least rule out ones likely to fail? (It seems like the problem of doing this from an investor standpoint is well investigated, and hard to do, but the employee standpoint is different).
It seems like the correct approach would be to make predictions on the future performance of a bunch of startups and track the results, in order to calibrate your predictive model, but one would need time to build up a prediction history. Short of this, there might be heuristics that are sort of helpful, ie. I’d guess that startups with more funding or more employees are more likely to succeed due to more people having confidence in them and having survived for some period of time already, but this also indicate that you are likely to get less equity.
It strikes me as implausible that the best way to make money in technology is to try to make the most money right away. So I would go a step further back than you and look for good learning opportunities rather than ones which will make a lot of money.
To this end, I think a company’s degree of sponsorship is often underrated when people are making decisions. I wrote more about this here.
Startups are an extremely mixed bag when it comes to learning opportunities. You will be given a lot more responsibility than you would in a big company, but you also receive less mentoring and you will have to do a lot more grunt work that would be outsourced to lower skilled people in a big company. My guess is that the average big company is a better employer than the average startup for people who are earning to give.
To the extent you do want to join a profitable startup, I would guess that it’s very hard to outperform professional investors. So if they’ve recently raised funding it’s probably best to just assume that valuation is correct, but it could be tricky if they haven’t raised recently. If they haven’t raised recently because they’ve got a good cash flow, you could look at EBITDA or revenue multiples; if they haven’t raised recently because they can’t find investors then that’s probably a red flag.
Although I’m a fan of this attitude in general, venture investment is not the ideal candidate for the efficient market hypothesis, and investors have very different deal structure from employees. Some notes:
VCs manage other people’s money, which means they’re basically buying options on the startups, not the startups themselves. As a result they do a lot of variance-chasing.
The market for venture investments is incredibly illiquid. It’s virtually impossible to short sell them, for instance, which inflates valuations.
The things that get traded in a venture deal are not just cash. A company that got valued at $10m by Sequoia is likely more valuable than one that got valued at $10m by a first-time investor. Similarly, a company that got valued at $10m in a round where the VC got a 3x liquidation preference is much less valuable than the equivalent with no liquidation preference.
Anecdotally, investors often do not know very much about the businesses they invest in and do not understand them well. My impression is that most venture investors are not much better than an index of startups, but mostly profit/stay in business because (a) the entire sector is growing and (b) they’re the ones with access to dealflow.
In summary, investor valuations are biased high, probably by a large factor IMO, and also have incredibly high variance. I would use them only with extreme caution.
Employee #100 seems a bit implausible. If you joined Dropbox as employee #100 it would be in early 2012, at which point they had just gotten a $4B valuation. It’s only gone up 2.5x since then—a mere 35% per year—so you probably wouldn’t have done better than a founder over the equivalent timespan. Especially once you take into account the many worse options that were in Dropbox’s reference class in 2012, like Fab.
That said, I agree that trying to forecast startups is probably a useful exercise—and maybe even possible to do historically, if you’re interested in ones as high-profile as Dropbox. It’s an open question to me how efficient the market is here (i.e., are companies with semi-obvious predictors of success likely to offer less equity).
I heard a rumor suggesting Dropbox was slower to hire than the typical tech company (i.e. a $4B company with <100 employees is somewhat atypical even in tech), though this may be what the norm is trending towards.
Great point. I would add that 35% annual raises are completely within the realm of possibility in direct employment as well.