When Cramer coined the FAANG acronym, Netflix was included. That group of companies was grouped together by investors due to their performance and marked dominance at the time, which has by and large continued.
In addition to their stock price, these companies also pay very well. In fact, if you are lucky to get an offer from Netflix, their aim is to pay you and keep you at "top of personal market". That is, they do not believe anyone will pay you more in total comp. Netflix does mostly, if not all, base salary as opposed to equity grants.
From all my friend in FAANG, this has seemed to be true, Netflix employees make more than anyone else I know in tech.
FAANG engineers definitely do, especially with a few years of tenure. My SQL-slinging data analyst friends at Netflix start at 350-400 base, and the sky is the limit. Now, if you don't work at FAANG, you're probably averaging in the 200-300 range total comp. A couple can easily clear 500k a year for 5 years, and still not feel comfortable buying a house the size of which I grew up in the Midwest (i.e., small).
In short, incomes are high, and so are costs. That being said, I've definitely come out way ahead by living here by staying somewhat sane with expenses.
Thank you for sharing this, these are fair comments. My experience has been more positive, but I see the truth in many of your points. For what it's worth, I have always been involved in controlled experimentation, and never anything related to ML or modeling. Our biggest issue is usually ensuring we are getting accurate counts of events, and when I see all the challenges in that, I wonder how anything more advanced ever gets done.
I don't know if this is true for many people who live here. There is a subgroup of my friends who live in the city and will drive almost every weekend for 30-60 minutes with their bikes and kayaks for recreation. If you avoid the popular trails and rec areas (e.g., Muir Woods), you can get somewhere very peaceful fairly quickly. I think the balance between city and access to nature works for a lot of people who live in the Bay Area.
Salary, bonus, healthcare, dental, 401K, office space, taxes, equipment, ... employees are not cheap. That figure would be competitive in the Bay Area for quality dev work.
It's true in most luxury goods. In this case, instead of Louis Vuitton, George Ault was the brand being replicated. High-end mechanical watches are another example, where the "right" name on the dial definitely affects the demand, even if lower-priced watches have nicer internals and finishing for far less cost. In fact, there is a whole group of consumers who knowingly purchase replicated pieces (some of which are getting indistinguishable from the real thing). A lot is in a name, in art and otherwise.
If your herb was shown to improve abdominal pain in a certain population through use of well-executed, replicated randomized control trials, I think it would count as medicine, yes.
For me, not having to worry about the "where" and "how" related to physical placement and connection of that Ryzen box is worth the premium and then some.
When I was in elementary school in the 80s, someone in our district retrofit a school bus with two rows of computers running along the sides. The bus would travel to each school in the district once a month or so. We would all walk out to the parking lot, board the bus, and use Logo. It was probably the first exposure to a computer most of the kids in my class had, and I have never forgotten it.
You're thinking about things correctly. The statistical and scientific methodology of clinical trials are by and large the same thing as A/B tests. I don't think anyone is drawing much from this sample of n = 11 except to say it's definitely not a miracle cure. No approval would normally be given to a new treatment for any study of this size.
If an A/B test took a form from almost 0% conversion to almost 100% conversion, you might only need 11 users to show that. But even in that case, the devil is in the details. Were your users really a completely random sample from your population, etc?
The other side of the coin is, can we say these are non-interesting treatments with only 11 subjects? Or are the studies too underpowered to say much of all?
While your point is a good one, meaningful clinical research is hard enough under normal conditions. Forms, enrollment, protocols, data collection, statistical analysis plans, reporting, etc. It's an entire industry used to operating in scales that often span years for a single meaningful study.
Imagine a treatment that would being people back to life. Surely if you saw that 11 times you would believe it worked. In fact, after n = 1, you would be so amazed you'd be thinking there is a trick. The point is that, while N is important, the N needed to uncover a signal differs wildly with the outcome you're trying to affect, specifically the outcome's variance. If one of these drug cocktails 'works' (which has many different definitions in clinical research), the signal may be obvious enough that n = 11 is enough to hone in on a promising treatment for further study. It's complicated.
It depends what hours you are trying to count. In the office? Remote? Actually 'working'? Including shuttle time? In my FAANG circle, small N, none of those values would be above 40 hours a week, except possibly if you include shuttle time. Certainly not anything approaching an average of 60-65 hours a week. That's averaging a 12-13 hour day, in a 10am-4pm core hour culture.
In this hypothetical trip to Japan, with airfare, lodging, food, drink, local transportation, and luxury knife purchases (including duty), a 25 USD checked bag fee is 'hefty' and a 'big problem'?