How did you get to equity packages being “routinely” worth millions when tech startups fail somewhere between 75% and >99% of the time (depending on estimates)?
Seems far more likely that startup equity will be worth zero to typical individual contributor employees, not millions
Case in point: 2 years ago i interviewed at a number of places with mind boggling valuations and most of the places I got offers from either no longer exist or laid off half their staff. It’s a lottery
By your own measure, if startups fail 99% of the time, shouldn't one value a $1M equity as $10k bonus? "Zero" does seem extreme, agree with the sentiment that "it's less than you think" but if you get lot of equity in a series-C startup, I wouldn't say that's equivalent to 0.
Probably not, because even in the 1% when the startup succeeds, there is probably some gotcha. It will turn out that your share was diluted, or your shares are not priority shares unlike the shares that your boss owns, etc... unless you are an expert, you have no idea about the dozen ways you can be screwed even in the unlikely case that the startup succeeds.
> just that it isn’t uncommon for options to be worth a lot.
You're deluding yourself here. On average, the vast, vast majority of equity options, _especially_ in the VC backed tech world, turn out to nothing for the employee.
You literally built a tool because there's so many variables, and in the majority of cases, all these variables do not align in a way that results in a payment.
This is almost the literal definition of "uncommon". It is uncommon for options to materialise into a large amount of value for employees.
I respect your tool, and I respect what you're doing. But you need to be honest with yourself and the rest of the world. If you want to help young or new people in this area, then don't perpetuate the myth that startup tech company options are statistically any better than a lottery ticket.
A piercing that takes months to heal and has its own potential side effects (infection for one) does not seem worth weeks of relief after which pain returns. The authors of this study therefore do not recommend this piercing for migraines despite the transitory benefits
“current evidence does not support daith piercing for the treatment of migraine, tension-type headaches, or other headache disorders.”
Team cohesion is important as far as being able to work collaboratively to finish a project, but it’s not clear that requires being in an office every day for 8+ hrs
I find that its easy to look back on memories and wish to go back. One day it clicked that I may look back on today and think the same thing. For me, the key is to try and be happy today, try not to look backwards and long for the impossible idea of revisiting those times, and try not to fear the future.
I'm not terribly familiar with Google Voice (it also isn't available in my country...), but they look similar in terms of functionality at this point in time. For me personally, the primary reasons to go with Relay would be that I'm already trying to move away from Google as much as possible for privacy reasons, that I'm already using Relay for email masking, and that Relay is explicitly focused on the privacy use case and will keep evolving in that direction.
I can relate to the privacy-focused goal of getting away from google however Google Voice is free. Sadly I think having a competing free Google product that accomplishes most of the same things is going to hurt adoption of the Firefox relay product (which is paid)
This is a little like programmers calling the programming language they invented and write in every day garbage. Separate from patient-reported histories, you medical doctors are the ones documenting these histories and hold the decision making power for how it’s done!
Your first sentence would be the equivalent of the inventor of patient history data keeping calling patient history keeping a garbage tool. Which is actually something that would be totally OK to do and say if suffixed with "and unfortunately so far nobody has come up with a better tool and it's not for lack of trying".
I'm assuming you didn't mean "you medical doctors" in the sense it's easy to read in. In any case, what you are doing here is telling one doctor that he is bad at the medical history writing and reading job when in fact he is the one telling you how he is able to spot other doctor's mistakes and trying to correct them. This is like telling one developer that he's bad at his job, that "you developers are the ones writing bad code and hold the decision making power for how it's done" when that developer is actually someone that tries to make things better both through his own maintainably written code (medical histories) and helping others in code reviews to make their code better and not let bad code get into Prod (finding errors in existing medical histories and trying to correct them).
That can be very discouraging, being thrown in with the bad apples. And even good apples can have a bad day or misunderstand something. But I guess you are perfect and have never produced a bug in your life.
This is a fallacy. At any point in history you can say “if X field was so good, we’d have Y by now”. In 1925 you could’ve said, “if biology’s understanding of bacteria is so good, we’d have antibiotics by now”. Within 5 years, they did.
There is certainly noise in healthcare data especially when patient-reported, but is it noise to say that a patient having X procedure later does or doesn’t have serious complications? Analyzes of medical care and their consequences can be evaluated and it’s not noise
And big healthcare data has lagged, partially because privacy concerns trump sharing. There are companies selling anonymized medical records for basically every American now though. Big data is coming
Big _bad_ data... Let's see how we fare in 5y, then. My prediction as a clinician with a special interest in stats: close to zero medical progress. But insurance priced by a ML algorithm, and much greater efficiency in coverage and claim denials.
Due to the Affordable Care Act (Obamacare), medical insurers have very little flexibility in pricing policies. There's not much point in using ML for pricing.
The first flu vaccine came about in 1945. Knowing as much as we did about viruses then, you might think we would have a cure for influenza (or the common cold) by now. Here we are almost 80 years later... big data may be coming but if takes that long it won't be in my lifetime.
Seems far more likely that startup equity will be worth zero to typical individual contributor employees, not millions