00:00 historically big Tech has been the go-to
00:02 destination for some of the smartest
00:04 software Engineers data scientists and
00:06 product managers in the world with the
00:08 bleeding edge of projects and the topof
00:10 the line salaries it's no wonder why
00:13 Superstars are drawn to these companies
00:15 or at least they used to be it turns out
00:18 that more and more Superstars don't want
00:21 to join big tech companies in fact just
00:24 recently Facebook was having a bit of a
00:26 hiring crisis when only half of their
00:28 top tiar job offers were being accepted
00:31 a large reason for this is that many of
00:33 these applicants weren't actually
00:35 looking to work at Facebook or any other
00:37 big tech company at all rather they
00:40 simply applied and went through the
00:42 interview process in order to get a
00:44 strong anchor from which they can
00:45 negotiate with the companies that they
00:47 actually want to join it's not much
00:49 different with the people who did end up
00:51 joining these companies either many of
00:54 them only see these companies as a means
00:56 to an end a way to gain experience build
00:58 a strong resume and climb the corporate
01:00 ladder before starting their own company
01:02 or joining a project that they're
01:04 actually passionate about this isn't a
01:07 new trend either for decades the startup
01:09 scene has been dominated by ex big Tech
01:12 employees but this trend has vastly
01:15 accelerated over the past couple of
01:16 years and who could blame them they're
01:19 just looking out for themselves and
01:21 making the best choices for their
01:22 careers but this brings up the question
01:25 of where does this leave big Tech as
01:29 they slowly lose their status as being
01:31 the destination for Superstars what will
01:33 happen to their Flagship products like
01:35 the metaverse and Google bard also what
01:38 will happen to the culture of these
01:39 companies as they slowly devolve from
01:41 being an engineering playground to being
01:43 just another corporate office filled
01:45 with politics Antics and egos well
01:48 likely nothing all that great so here's
01:50 an in-depth look at the big Tech brain
01:52 drain and what this means for these
01:54 companies and the tech industry at
02:05 starting off with the biggest question
02:07 why in the world are people not
02:09 accepting these job offers is the
02:11 compensation just not high enough what's
02:13 going on well this breaks down into
02:16 three major reasons starting off with
02:18 the big Tech offers not being all that
02:20 hard to come by that's probably an
02:23 extremely controversial statement
02:25 because for you or me scoring a big Tech
02:27 offer might be borderline impossible
02:30 but for the people who actually end up
02:32 getting these offers it's often not all
02:35 that crazy in fact it's not uncommon for
02:38 them to apply to 5 to 10 companies and
02:40 to get job offers from all of them if
02:42 not most of them if you're wondering who
02:45 these people are I would recommend
02:46 checking out teamblind.com over there
02:49 they're everywhere literally half the
02:51 post on blind or something along the
02:53 lines of should I take a 667 offer at
02:55 meta wait for an L7 offer at Google or
02:58 keep my L7 Amazon job let me know this
03:02 is especially true once you already have
03:04 your foot in at one of these companies
03:06 after that you don't even have to apply
03:08 to other fan companies instead
03:10 recruiters from these other companies
03:12 will basically beg you on a weekly basis
03:14 to come interview with them so the
03:17 reality is that for a lot of superstars
03:19 once they're already in the industry for
03:21 5 or 10 years scoring another big Tech
03:23 job offer isn't some sort of Holy Grail
03:26 in fact they're often over the whole
03:28 idea of working at big Tech in general
03:31 which brings us into reason number two
03:33 the compensation isn't enough with
03:36 slowing growth and high interest rates
03:38 big tech companies have largely cut
03:40 compensation especially stock comp which
03:42 is the most lucrative part of these job
03:44 offers but that's not what I'm referring
03:47 to when I say that the compensation is
03:49 not enough rather what I mean is that
03:52 higher compensation alone is not enough
03:54 to convince Superstars to join the
03:57 reality is that they're often already
03:59 multi- millionaires because of how
04:01 expensive these cities are it's often
04:03 the case that they're not able to retire
04:05 even with $2 or $3 million in savings
04:08 but while they may not be able to retire
04:11 they're definitely well off and never
04:13 need to worry about money again as such
04:16 they don't make their next job move
04:18 purely based on who is offering the
04:19 biggest salary they'd often much rather
04:22 take a lower job offer with a lot more
04:24 scope and growth opportunities I mean
04:27 just think about it while it might sound
04:28 cool from the out side to say that you
04:30 work on the YouTube iOS team what does
04:33 that actually entail it's not like
04:36 you'll be coding out the new shorts feed
04:38 or the new recommendation algorithm from
04:40 scratch rather you'll probably be
04:42 working on fixing bug 2001 regarding
04:44 Community post or debugging some issue
04:46 with dark mode and that's not Google's
04:49 fault that's simply the reality of
04:51 joining any project that's as mature as
04:53 YouTube now I can already hear a bunch
04:56 of you saying that you'd love to fix
04:57 some bugs and earn that much
05:00 but that's not exactly how the high
05:02 Achievers these companies are targeting
05:03 think they'd much rather join a smaller
05:06 fintech company or an AI company where
05:08 they can actually play a larger role in
05:09 product development even if the
05:11 compensation is slightly less and that
05:14 brings me into the third reason that
05:15 Superstars are no longer interested in
05:17 big Tech with this compensation
05:19 potential sure compensation at smaller
05:22 companies may be lower because the cash
05:24 portion is lower but they have far
05:26 greater growth potential for example
05:29 would you rather th get $100,000 worth
05:30 of Google stock and work on Google bard
05:32 or $100,000 worth of open AI stock and
05:34 work on chat GPT from that perspective
05:38 the answer is obvious so whether you're
05:41 in it for the money or the role itself
05:43 big Tech offers simply aren't all that
05:45 appealing especially when they're not
05:47 that hard to come by but if Superstars
05:50 are no longer joining big Tech who
06:01 ironically the question of who is
06:03 getting into big Tech today is extremely
06:05 easy to answer and breaks down into
06:07 three categories starting with the
06:10 sellouts the people who always have
06:12 their eye on the ball for increasing
06:14 status job title and compensation and I
06:17 want to note that I'm not criticizing
06:19 these people by any means they're just
06:21 looking out for themselves as they
06:22 should be moreover these people are
06:25 usually more than qualified for these
06:26 roles as they're just as smart as the o
06:29 G Superstars that's why they were able
06:31 to crush the interviews and to get in
06:34 but their intentions are far different
06:36 they're less interested in using their
06:38 skills to build the best products and
06:40 are more interested in using their
06:41 skills to maximize career progression
06:43 and compensation a prime example of such
06:45 an employee is Quant Engineers if you're
06:49 not familiar with Quant Engineers their
06:51 jobs are to use their extensive
06:52 knowledge and Applied Mathematics data
06:54 models algorithms statistics and
06:56 calculus to create software that can
06:58 trade the financial markets essentially
07:01 Quant Engineers are creating insane
07:03 algorithms and software just so that
07:04 Financial firms can scam 0.5% on natural
07:07 gas features or a few cents on bid ask
07:10 spreads obviously super monotonous Soul
07:13 draining work but it pays extremely well
07:17 after all making half a perc on a
07:19 billion doll natural gas position adds
07:21 up to $5 million in profit every single
07:23 trade and it seems that Fang companies
07:26 are heading in the exact same direction
07:28 especially as they keep pulling back
07:30 from moonshot ideas eventually all
07:33 that's left are monotonous maintenance
07:35 and refinement jobs which only appeal to
07:37 people who are really only in it for the
07:39 paycheck but at least these candidates
07:41 are highly qualified the same cannot be
07:44 set about on next category of
07:46 opportunists which are the game players
07:49 these people don't necessarily have
07:51 strong engineering backgrounds or a
07:52 passion for Tech they're often stuck in
07:54 a corporate job that pays all right or
07:56 even a low paying job but what what they
07:59 do have is a massive amount of drive to
08:02 change their situation and that's when
08:04 they hear about all these people in big
08:06 Tech earning massive salaries straight
08:08 off of college with very little
08:09 experience naturally they decide to make
08:12 the switch but their strategy usually
08:14 has less to do with learning the skills
08:16 required for the job and more about
08:18 learning the skills required for the
08:20 interviews you know grinding out lead
08:22 curb problems creating compelling
08:24 stories to tell hiring managers notw
08:26 workking with as many people as possible
08:27 and just playing the game in general
08:29 General to be honest this strategy is
08:31 way more effective than just being a
08:33 good engineer this is why a lot of great
08:36 Engineers are never able to break into
08:38 Fang they're just not able to sell
08:40 themselves but while this is a
08:42 phenomenal strategy to get in the door
08:45 the effectiveness of these individuals
08:46 once they get in the door is a lot more
08:49 questionable I mean how much does
08:51 solving Le cord problems really
08:53 translate into building highly scalable
08:55 Solutions or new Innovative products
08:57 likely not all that much
08:59 but at least these guys are largely here
09:01 by choice which cannot be said about our
09:04 last category of opportunists which are
09:06 the groomed now I don't want to throw
09:09 shade at Asians but this is extremely
09:11 common within the Asian Community
09:13 basically starting from age five parents
09:15 will push them to join a bunch of STEM
09:17 related EC's coding camps and take a
09:19 bunch of Cs classes this way by the time
09:22 the kid is 18 regardless of Interest
09:24 they're pretty good at coding from there
09:26 they'll usually attend a top public
09:28 universe if not an IV league go where
09:31 they major in CS before they crack their
09:33 way into Fang putting the whole ethics
09:35 of all of these aside this doesn't
09:37 exactly lead to the healthiest Workforce
09:40 you basically end up with hundreds of
09:41 thousands if not millions of sellouts
09:44 game players and groomed individuals
09:46 which brings us into the question of
09:57 next how exactly each of these groups
09:59 got here is vastly different but they
10:02 tend to end up having very similar
10:04 individualistic motivations job security
10:07 maximum pay and promotions which again
10:10 are not bad goals to have they're just
10:12 not all that great for the company this
10:15 sort of environment is what leads to
10:17 entrenched workforces with nothing but
10:19 office politics much of these employees
10:21 efforts don't go into creating the best
10:23 products or efficiencies possible rather
10:26 they go into outperforming their
10:27 teammates and pandering to their bosses
10:29 even if that doesn't lead to the best
10:31 results for the company the reality is
10:34 that most of these guys don't give a
10:35 hoot and Hell about the company other
10:37 than the status that it gives them and
10:39 their growing stock price in fact many
10:41 of them don't know how their role is
10:43 even linked with the end user most
10:45 people would hate this work environment
10:47 but these guys thrive in this
10:49 environment many of them have been
10:51 dealing with such environments for their
10:53 entire careers and by the time they're
10:55 in their 40s and 50s they're masters of
10:57 managing politics and getting exam
10:59 exactly what they want this is how you
11:01 end up with tech leaders who have no
11:03 formal Tech background often times
11:05 they're from the marketing department or
11:07 the finance department and their
11:09 playbook as CEO is usually just
11:11 infinitely cutting costs and increasing
11:14 efficiencies but this can only take
11:16 these companies so far as they Veer
11:18 further and further away from what
11:19 initially made them successful and soon
11:22 enough you end up with your next
11:24 generation of intels cisos and ibms
11:28 meanwhile the super stars who made Fang
11:29 into what it was move on to up and
11:31 cominging companies with new promising
11:33 Tech and they helped scale these
11:35 companies to new leagues far beyond what
11:37 the previous generation was able to
11:39 achieve and the psycho repeats all over
11:42 again not only is Fang experiencing a
11:45 brain drain but they're also reaching
11:47 Market saturation for the first time
11:49 check out this video to learn more but
11:51 until then I'm Harry I'll see you guys
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