I don't want to derail the conversation, but OPs career path really stood out to me.
He graduated in 2016, worked at Google in Bay Area, and now is joining a startup at a VP level.
I graduated in 2008, obtained a PhD in 2014 in a no name EU university, worked in odd companies for a while and joined FAANG 4 years ago as a mid level developer, where I am still ATM.
Looking at this disparity I wonder what could be possible explanations:
* OP is a beast and has grown very quickly in a short time.
* I'm particularly inept and I'm growing very slowly.
* Working in the right conditions (e.g. Bay Area, Big Tech, right team) can greatly accelerate your growth.
Don't despair. I work for a FAANG and have previously worked at startups. Title inflation at startups is a huge factor. In fact, titles are not equivalent between any two companies. I have seen startup CTOs (even series A) transition to senior engineer IC roles at a FAANG.
As you identified, location is the next big factor. If you are still in Europe, my advice is to leave or to start your own company there. If you are working for primarily US based companies in Europe there will always be a limit to the level of exposure you get to leadership and to how fast you can rise up the hierarchy.
Finally, don't discount Eric's profile. Through some combination of his public profile and professional work, he's established a reputation and following. That is just as important as any hard engineering work in securing a senior/leadership role.
I'm glad someone validates my belief that "location matters". I moved to the US 2 months ago after 2 years in Covid VISA limbo working remotely for a US team. Settling down has been extremely painful so far but I hope it's worth the effort in the long run.
Yeah, and the way the author presents themselves speaks volume too. There is a real pride in this essay. You can see how the author casually drops big names and insights like it is a fact.
Why does valley culture makes it seem like everything is possible and anything innovative can happen soon? The innovation in AI really seems like it is being made on a thin line of engineering and compute. It doesn't happen overnight. It requires some people working through and through to pull it off. These days it requires collective contribution.
> The innovation in AI really seems like it is being made on a thin line of engineering and compute.
This perfectly echoes my own thoughts. The advances being trumpeted in AI are functions of hardware advances that allow us to have massively overparameterised models, models which essentially 'make the map the size of the territory'[0], which is why they only succeed at a narrow class of interpolation problems. And even then nothing useful. That's why we're still being sold the "computer wins at board game" trope of the 90s, and yet somehow also being told that we're right on the verge of AGI.
(OK, it's not only that. There's also a healthy amount of p-hacking, and a 'clever Hans effect' where the developer likely-unconsciously intervenes to assure the right answer via all the shadowy 'configuration' knobs ('oversampling', 'regularisation', 'feedforward', etc). I always say: if you develop a real AI, come show me a demo where it answers a hard question whose answer you - all of us - don't already know.)
[0] Or far larger, actually. Google the 'lottery ticket hypothesis'.
Eh, if you boil all research in AI/ML down to the binary of "AGI or bust," then sure, everything is a failure.
But, if you look at your smartphone, virtually every popular application the average person uses--Gmail, Uber, Instagram, TikTok, Siri/Google Assistant, Netflix, your camera, and more--all owe huge pieces of their functionality to ML that's only become feasible in the last decade because of the research you're referencing.
Sorry, I should have been clearer. I obviously concede that stuff like applying kNN over ginormous datasets to find TV shows people like, or doing some matrix decomposition to correlate ('recognise') objects in photographs, is obviously useful in the trivial sense. It has uses. It wouldn't exist otherwise. I was more thinking on a higher level, about whether it has led to any truly epochal technological advances, which it hasn't.
Machine learning / neural nets also (like I said) get to claim credit for a hell of a lot of things which are just products of colossal advances in hardware – simply of its becoming possible to run statistical methods over very very large '1:1 scale' sample sets – and not due to a specific statistical technique (NN) which is not remotely new and has been heavily researched for about 40-50 years now.
These are engineering marvel! This is engineering at it's finest. Applied math at it's finest. So it's not a failure.
The way people hype AGI/AI/ML whatever undervalues the actual effort behind these remarkable feat. There is so much effort being made to make this work. Deep learning works when it is engineered properly. So it is just another tool in the toolbox!
Look at how graphics community is approaching deep learning. They already had sampling methods but with MLPs (NeRFs), they are using it as glorified database. So it's engineering!
I want to underscore that AI/ML/DL research requires ground breaking innovation not only in algorithms but also in hardware and software engineering.
I disagree, there are plenty of amazing advancements in the last 2 years you can't write off like that (especially Instruct GPT-3 and Dall-e 2). For example I have worked on a ML project in document information extraction for 4 years, and recently tried GPT-3 - it solved the task zero shot.
> show me a demo where it answers a hard question whose answer you - all of us - don't already know.
For that, we need artificial comprehension, which we do not. Artificial comprehension, the ability to generalize systems to their base components and then virtually operate those base concepts to define what is possible, to virtual recreate physical working system, virtually improve them, and with those improvements being physically realizable is probably what will finally create AGI. We need a Calculus for pure ideas, not just numbers.
I'm not really sure what you mean. This seems to be another instance of the weirdly persistent belief that "only humans can understand, and computers are just moving gears around to mechanically simulate knowledge-informed action". I may not believe in the current NN-focussed AI hype cycle, but that's definitely not a cogent argument against the possibility of AI. You're confusing comprehension with the subjective (human) experience of comprehending something.
Tech has a bad habit of conflating comp/prestige with skill. I have no doubt the OP is quite good at what they do, but you not being where OP is does not therefore imply you don't have skill.
Unfortunately the tech world is not really a meritocracy.
When I look at my own circle of technical people the most incredible ones from a pure technical ability are divided between working at FAANG making 500k+ and working a relatively unknown companies making ~200K or less. One of the most mindbendingly brilliant people I know is working in relative obscurity, known very well only among other people that are top in the field, but their resume looks very ordinary compared to their behind the scenes contributions to major projects.
Managing a career in tech is largely independent from technical skills and abilities. I have met a shocking number of people making lots of money at prestigious institutions that are "meh" as far as technical ability goes (of course there's some great ones as well), and have met plenty of brilliant people working relative obscurity.
The success is largely a function of both background (Brown does beat a "no name EU university") and personal desire to have a prestigious career. There is a lot of self promotion going on in this piece, in fact the OP has already convinced you that they might be just a wildly better person than you. If they can convince you they are this amazing, then they also can convince the leadership team at a start up. But do recognize that their skill demonstrated so far is only in convincing you of this.
There is more to being a great tech employee than just being 'brilliant' at the hard skills. Soft skills are just as important, and play a role behind why I have been promoted more than peers who surpass my skills ten-fold. Some people also don't want to be in management.
We all have different trajectories and choices. This comment makes it seem like if you aren't a technical wizard then you might as well be useless. This is not reality
Probably a combination of 1, 3 and 4. There are tons of talented people even at the top but the stars have to align to achieve more than your expected value.
PhD -> low-level dev -> FAANG mid-level is nothing to scoff at so you're doing pretty well.
Also, the author grew up in the Bay Area (early exposure to how the SV ecosystem works), went to an Ivy league school (opens doors to top internships), landed those internships, and got a masters. All those things, but especially the internships, can help fast track your early career.
I’d take some things here with a grain of salt, like “low 7 figures compensation (staff level)” at FAAN (can eliminate G because they’re not likely to hire him back at L+1 immediately after he quits). ML is still somewhat hot, but 7 figures is an outlier for staff-level comp.
Nobody there is reporting $1M+ offers for staff level. While I’m sure it’s happened, it’s pretty far outside the staff pay band (excluding equity gains during the 2020-2021 market run-up, which are sadly behind us) and would be a truly exceptional offer even in the current climate. That, plus the fact that it sounds like he didn’t get many formal offers (“I did not initiate the formal HR interview process with most of them”) and wasn’t pitting offers against each other, makes me skeptical.
Without knowing the deatils, it is mostly #4, with a good doze of #3, and potentially a decent amount of #1.
Basically, yeah, small/not-yet-massive startups have insane overinflation in titles. Had plenty of former college classmates who became "VPs" or "staff engineers" at super small startups a couple years out of college. Getting plenty of recruiter messages on linkedin myself for "staff engineer" positions at random startups, despite me not even being a senior at a FAANG yet, and only being about 4.5 years out of college.
Another thing is, no matter how smart or hard working you are, being in the right place at the right time is extremely important. It won't help much if you lack skills, but being in the right place at the right time is like a force multiplier on your skills and the work you do. Which is partially why most of the big opportunities are still heavily concentrated in a few geographic spots (despite there being no tangible technical need for that).
Don't beat yourself up over it, titles don't mean that much. You are able to start a one-man-shop LLC and call yourself a VP, a director, or whatever else you want. The real question is, with that title, are they being compensated as much as you are? If they decide to quit and get a job at a "regular" tech company after, will that VP title translate into anything more than an L4/L5? Just some food for thought.
If it makes you feel any worse, a fresh-college grad just worked his first month at any job on my team at a midwest company and got an offer at Google making more than I am now. I have over a decade of experience and endless Cloud Architect certs (all 3 clouds) as well as a background in finance.
Right place, right time + talent + willingness to take risk.
I'm the same deal as you. I don't really have any problems with it. I worked at a startup and titles are much fancier but you don't get to ship to a billion users. Also the infrastructure can suck real bad.
Competing within a giant company for perf ratings feels like school and I'm over it. But the other parts of the job are great.
Probably 1 + 3 + 4. He didn't just work at Google, he worked at Google Brain. And with only a Bachelor's, so I assume he's is both very smart and got very lucky.
I was a VP at a startup (~200 people) before I was 30 without a PhD. It was all BS and I had less manager qualifications than a FAANG line manager. I got lucky.
It's clear from the blog post that the author is in the same boat. They lament CEOs not having time to do research but took a VP position. An actual VP doesn't have time do research so they're clearly not an actual VP. So they're likely a tech lead with an inflated title.
1) The PhD takes a huge hit on your opportunity cost.
2008-2014 is 6 years of time; for me, it was the delta between starting my career as a junior engineer and becoming a tech lead at a hot unicorn which let me pivot to a CTO role at a small startup.
2) Academic credentialism has real effects.
This guy did a CS degree at an Ivy in the US. He has been set up for commercial success in the US tech industry through a halo effect you cannot also access unless you gained access to that institutional grooming at the same age. By choosing to do that PHD in EU (and a no name one at that), you forfeited that access.
In my experience, while the effect of this goes down over time, it has extremely strong launch + early compounding effects.
3) Risk tolerance can work to your benefit or against it.
You are working at a FAANG which is the safest and most cash lucrative option. In all likelihood, you have a great WLB and now a great blue chip brand on your resumé. However, the cost of this is that you're generally not going to get access to projects or culture that, by virtue of your participation, set you on an extremely steep growth path.
To get access to that, IMO, there's no real alternative to achieving strong outcomes working at a startup. Of course, that can be hard to do -- how do you figure out which ones are future winners, and how do you get them to let you come on board? I have no great answer rather than early career trial and error (accepting some of it will work out poorly and uncomfortably so).
I wouldn't say that "OP is a beast" per se, but it's much more likely that they have been groomed (working in the right conditions) in ways that you may not have. And yes, startups titles are not comparable to big company titles. It's apples and oranges.
The company he joined is a Series A startup, so absolutely an early stage company where whether you're VP/CXO, you're functionally going to be doing a player/coach role at most with tons of strategy baked in. But I wouldn't call that inflation, per sé. Sure, it's not the equivalent of being an experienced people leader and executive at a big corporation manning a giant organization at its helm. But you are often times in charge with significantly more responsibility and do not have bureaucratic friction and slow pace to hide behind. Doing a startup is just different. It's insanely risky, overall has poor risk adjusted rewards, and often is a magnet for shady characters. But if you can filter out the wheat from the chaff, you get access to the best career opportunities available, bar none.
> Working in the right conditions (e.g. Bay Area, Big Tech, right team) can greatly accelerate your growth
This is the answer. I have grown more in ~7 years* of random SFBA startups than I did in the previous 13 years of career in Europe. Just because the kind of startup that's a dime a dozen over here is a once in a lifetime opportunity back home.
To put this contrast into numbers: In 2021, during the pandemic while "SFBA is dying" was the mem, the Bay Area raised as much startup investment as all of Europe.
*I wasn't as career aggressive as I could've been, mostly for visa-related reasons.
Everyone starts from a different position. I don't think it's worth letting yourself get irritated or depressed by other's success. Just try change the position you're at to a better one.
I know a former Amazon Engineer.
After working at Amazon as a mid level engineer, co-founded his own startup in Mexico, as CTO.
It's a startup... titles in a 50 people organization don't compare to 50,000 people organization titles.
I'm sure you can go and be a VP at a startup too, if that's what you want to do. Just go and network at Incubator, Investor, & Entrepreneur events/meetups/organizations, and come up with an idea & customers, then execute and try to get customers on board... rinse and repeat.
He graduated in 2016, worked at Google in Bay Area, and now is joining a startup at a VP level.
I graduated in 2008, obtained a PhD in 2014 in a no name EU university, worked in odd companies for a while and joined FAANG 4 years ago as a mid level developer, where I am still ATM.
Looking at this disparity I wonder what could be possible explanations:
* OP is a beast and has grown very quickly in a short time.
* I'm particularly inept and I'm growing very slowly.
* Working in the right conditions (e.g. Bay Area, Big Tech, right team) can greatly accelerate your growth.
* Startups have a big title inflation.