
High Salaries Are Weapons in the AI Talent War - angpappas
https://www.bloomberg.com/news/articles/2018-02-13/in-the-war-for-ai-talent-sky-high-salaries-are-the-weapons
======
bsaul
My personnal experience with data scientist and startups is that they're hired
much much too early, when the product has a lot more fundamental issues to
solve, and only because it looks cool to say that you're doing AI.

In practice, they're often frustrated for years by the lack of infrastructure
to work on their ideas, but live with it because life is good.

I suspect AI today is like big data ten years ago : a lot of company think
they need AI, but in fact what they need is a good product and a few algorithm
requiring high-school level maths.

That could explain the shortage...

~~~
retbull
How would start ups compete well with those salaries?

~~~
dsacco
They can’t, and don’t. The secret is that experienced PhDs (mostly) dominate
the high end of “AI” hiring, but don’t have much title or departmental
differentiation from people who are “only” specialized software engineers in
top tech companies. But this isn’t very well known, large companies want to
recruit as much talent as they can regardless of role, and startups want to
compete _on paper_ \- therefore, you have the following effects:

1\. Startups give whatever sexy title they want to the people they can afford,
which makes titles fairly useless, because they almost never can afford the
talent commanding “sky-high” salaries.

2\. Within large tech companies like Google and Facebook, it’s hard to
immediately tell which of the many data sciencey, machine learning-y titles
correspond to the truly stratospheric salaries versus the engineers that work
with those roles. For some teams, like DeepMind, Google Brain or FAIR, it’s
easier to tell. But for others it’s a mixed bag.

For comparison, see the fashionable term of art “quant” in the financial
industry, which has similarly devolved into marketing and a bimodal
distribution. As a rule of thumb, you generally can’t trust AI titles or
salaries at startups unless those startups are really known for their talent;
further, you can safely assume that, at companies capable of paying for top
talent, the very impressive salaries belong to titles which seem the most
exotic and out of reach for general engineers in the job description.

Fortunately startups don’t really need to compete for top talent as a genuine
technological differentiator, they just need to engage in signaling, so this
is mostly a non-problem. Startups almost never have problems usefully improved
by the cutting edge of machine learning research, and can instead use off the
shelf tools and existing software to accomplish the same things. Frankly, it’s
exceptionally rare for a startup to even have the massive data, pipeline and
munging infrastructure requisite for actual research.

~~~
pm90
This actually makes a lot of sense. I have a lot of friends employed as Data
Scientist or Data Engineers who don't seem to be able to explain exactly what
they do, or describe what "research" they are doing. From what I understand,
it seems like they are designing pipelines that ingest data, run an off the
shelf AI algorithm and display nice graphs. There is a lot of pipelines you
can design for different kinds of data so I imagine they always have something
to do.

------
eberkund
This is an interesting subject, because intuitively one wants to make the
comparison "Is Conan O'Brein paid a multiple of how many times funnier he is
than a local standup?". But economically what's important isn't how funny he
is, but how many viewers he can draw. So then you might ask, does Conan draw
30x more viewers than some no-name comic? But that also isn't the comparison
that matters. If no-name comic can draw 10M viewers, but Conan can draw 20M
viewers then should he be paid 2x as much? It depends on the costs for the
rest of the show. If a show costing $500K to produce and $10K for a no-name
host earns 1M then if they were to replace the no-name host with Conan and
could earn $2M per show, it would make sense to do that as long as you weren't
paying more than $1,010,000 (101x the no-namer) for Conan to host the show.

The point I am trying to make here is that these figures vary from industry to
industry and from job to job, I could have conversely changed these numbers
around and shown that it doesn't make sense to pay Conan a huge multiple of
the no-name comic's pay. For example, if the revenue does not increase
substantially (like from 1M to 2M) or if the salary of the person in question
makes up a much larger portion of the overall expense of the company.

~~~
bkohlmann
I think it's more of a winner takes all phenomenon in the Conan case. Usain
Bolt makes millions because he's a tenth of a second (or less!) faster than
many other people.

Those other people - with the exception of maybe Justin Gatlan - make a far
more modest salary. It's less than a 1% difference in performance that leads
to orders of magnitudes differences in outcomes.

~~~
baybal2
Reminds me my first year as a webdev. I was extremely lucky to ride the apex
web 2.0 hype/insanity wave. Now I can't believe myself that I, as a 21 years
old and just 3 years of for-profit programming experience, got CAD 85k on
first real job Canada for just jquery animations.

Later down the career path in Canada, I was frequently asked "you worked for
guy A and B, than must've been hell of a job?" or "how I got there to begin
with?" All of them dismiss my explanation that "I just used to be on line 1 of
google for thing A"

The back side of the coin? The moment web 2.0 became a more of an "in-house"
production with mid-to-big sized companies that no longer needed a "hired gun"
outsider, and hype wave moved to other things I really hit a wall. Actually,
on my next 2 jobs I took salary in $70ks and was contemplating selling major
life assets after my last employer in Canada was unable to extend my work
permit.

Things went much better after I scaled down my appetites and stopped looking
for employment with companies obsessed with "rockstar hiring"

------
jxub
I suspect there may be some sort of hype in the AI area regarding salaries.
Sure, there are a couple of rockstars, but I suspect that competition is
really intense and compensation in ML area has an even more acused power law
distribution than general vanilla software engineering that doesn't face as
much pressure from maths, stats and other grads that look for a career to
apply their quantitative skills.

I think that in the real world paradoxically math skills are easier to find
than solid software design and development abilities. It may stem from the
fact that the first one is taught quite well in school while the other one is
more about individual learning and sometimes a contrarian stance to the system
(can be reflected even a somewhat childish "I'll learn Haskell because the OOP
and Java suck!") which is harder to find and therefore, more valuable.

~~~
JumpCrisscross
In my experience with AI start-ups, if you have a rockstar CV in your deck you
get funded. If you don't, you don't. It is very difficult for non-experts to
evaluate the competence of budding AI teams. The best heuristic, track record,
thus prevails, which in turn attaches a lot of value--from the company's
perspective--to that single CV.

~~~
wastedhours
I'd argue this line as well. A lot of the earlier stage startups who don't
need this talent are trying to secure it because it offers them that halo of
prestige to grease the wheels of fundraising.

A $400k salary is nothing if it makes it easier to unlock $XXm in funding.

~~~
ryandrake
I’d laugh if I wasn’t crying. I narrowly escaped high school where the popular
kids and “school celebrities” won. All that studying in university, all those
labs, grinding that entry level job, building my skills, grad school, more
hard work... and at the end of the day the popular kids inevitably win again.

~~~
willbw
I don't understand the comparison. Aren't the highly salaried AI experts also
people who undertook a lot of hard work to get there?

------
inputcoffee
A lot of the comments here are skeptical of the claims in the article. (This
is how comments should, and do, function).

However, for all the reasons that the article may be wrong

\- the shortage is temporary

\- the shortage is overblown

\- the shortage is illusory

\- the shortage doesn't apply etc

Please compare with the situation for other high earners (CEOs, Entertainers
and Bankers). Can the same arguments be made for them? (Conan O'Brien is
funny, but he isn't 30x funnier than the person at my local comedy club?)

The work of an AI researcher is mechanically reproduced so a 1% benefit can be
enormous. It could be the case that the competition isn't for more of them,
but for the best of them.

~~~
metalrain
Utility increase is not linear with cost increase. Conan or any other valued
expert may not be 30 times better, maybe just something like 1.5 - 5 times
better. But I do agree that demand will be satisfied in a long term.

~~~
pc86
Conan also gets paid what he does because his audience is millions or tens of
millions of people. The guy at the comedy club who is _funnier_ than Conan but
doesn't have the audience makes nothing.

------
CabSauce
There's a pretty big difference between AI researcher and applied Data
Scientist. Most companies don't need to develop novel algorithms, they just
need to be able to apply what's already available.

Fortunately, the applied DS jobs don't really require a PhD in machine
learning. A master's in CS, stats, etc is usually plenty.

~~~
hodder
What kind of salaries are MS applied Data Scientists commanding? I am very
interested in enrolling in Georgia Tech's OMSA, but very little graduate
statistics exist for these types of programs and job searches usually don't
provide compensation. Further, the vagueness surrounding the name “data
scientist” clutters up the information that is available.

~~~
pc86
It's not particularly popular to say here but the reputation of Georgia Tech
has decreased more than little bit since the OMSCS program started.

~~~
bitL
Why so? Their CS program is now #8 in the world and CS research #1 (at least
in one of those "proper British rankings").

------
otakucode
This is the sort of thing which was starting to happen with software engineers
way back in the 1990s as the Internet started growing explosively. It was
stopped by the large tech companies engaging in illegal wage-fixing for years,
setting the standard for software engineers being paid rates divorced from the
value they create. Sure they got busted for it decades later, but by that
point the danger was passed and astronomical profits assured.

~~~
akhilcacharya
I mean new grads still regularly make more than 180k at top tech companies...

~~~
jcadam
Hmmm.... wonder if I could pass for a new grad. Would need to do something
about this grey hair. And the wrinkles. And the attitude.

~~~
akhilcacharya
I mean, even lower-tier companies like Amazon pay that much for SDE2's these
days. Its just a function of how much wages have gone up recently.

~~~
jcadam
Ah well, I'm making ~$140k (salary) here in Florida. The Nerd Wallet COL
calculator says that's equivalent to ~$260k in San Francisco (or $210k in
Seattle), so maybe I'm doing alright :)

------
throwaway713
As someone with a PhD in an engineering field (but currently working as a data
scientist because all of my research was computational science), I wonder if
independent machine learning publications would help with getting one of these
jobs or if the CS PhD from a brand name school is an absolute requirement. I
have 8 publications in my field, so I’m familiar with the peer review process,
and it seems that with (a lot of) time permitting it might be possible to get
a couple of NIPS presentations or JMLR papers. But I don’t know if
publications alone would be enough to get hired for these AI positions.

I had a hard time making the decision when I chose an engineering PhD over a
CS one, but 6 years ago machine learning hadn’t taken off like it has now, and
engineering / hard science prospects seemed brighter at the time. If I had
known it was going to become this big and this interesting, I definitely would
have gone for CS instead.

~~~
throwawayjava
_> but 6 years ago machine learning hadn’t taken off... I definitely would
have gone for CS instead._

I thnk you're doing it wrong because that's _exactly_ why there's so much
demand for CS PhDs right now.

Rewind to mid-2000's when the CS postdocs/phds graduating over the past couple
of years were choosing to major in CS. They were warned that everything was
being outsourced to India and advised to choose a "real" engineering field, or
perhaps finance/physics/math. It's hard to imagine today, but lot of smaller
colleges/universities were killing CS majors back in the mid 00's!

So not only is there a shortage of CS PhDs in the pipeline, but the ones that
made it through came in with a burning passion for the science (as opposed to
the money/hotness). This combination of input bias and restricted supply is
what makes the current labor market so damn hot.

In 3 years, that will invert, and some major struggling to justify its
existence because it's hard but has "no future" will blow up. Rinse and
repeat.

------
syllogism
This is a PR piece put together by a company that offers AI services. Their
pitch is that hiring for AI is incredibly expensive --- so you'd better
outsource to us.

------
thisisit
> Designing AI systems requires a hard-to-come-by blend of high-level
> mathematics and statistical understanding, a grounding in data science and
> computer programming, _and a dose of intuition._

Doesn't intuition to resolve a domain specific problem require domain specific
knowledge?

~~~
cosmie
Not necessarily. Or more precisely, the intuition doesn't have to be in the
domain itself.

I've done operational improvement work across supply chain/inventory
management, marketing, digital analytics, ecommerce, healthcare revenue cycle
management, and call center operations. In almost every case I started with
little if any direct domain knowledge. The intuition that was valuable for my
work was around systems-oriented thinking applied to business processes and
being able to quickly suss out weak or suspiciously opaque areas of the
system. The necessary domain knowledge to do so was always picked up from
domain experts as I went along.

In fact, taking a naive approach on domain knowledge has always worked in my
favor to uncover invalid assumptions that those with domain knowledge just
accepted without question.

~~~
v3rt
What kind of work has exposed you to so many areas; are you a data consultant
(and if so do you work for yourself or a bigger shop?)

~~~
cosmie
It was a bit of a winding process, but essentially early on in my career I
learned that I fit this[1] description very well. And stumbled upon the same
fact that patio11 did: it's incredibly hard for most companies to consistently
fill that type of role. And now that I have a history of succeeding in those
types of roles, it's a lot easier to talk my way into new ones.

That said, you're spot on that the core of what I do is data consulting,
although most of it falls under a domain-specific name and has been W2,
internal consulting roles. I'm actually in the process of switching my full
time role to a less demanding one so I can focus on ramping up my actual
consulting business.

~~~
cosmie
Whoops, just realized I left off the reference and it's too late to edit. Here
it is:
[https://mobile.twitter.com/patio11/status/936616624378978304](https://mobile.twitter.com/patio11/status/936616624378978304)

------
CodeSheikh
Any current/recently graduated CS students here who can confirm that students
are required to take more mandatory Stats/Math classes in core CS curriculum
instead of just electives?

"Designing AI systems requires a hard-to-come-by blend of high-level
mathematics and statistical understanding, a grounding in data science and
computer programming".

EDIT: Improved readability

~~~
realslimjd
At UChicago CS didn't have a mandatory core of statistics or math classes, but
all the AI/ML classes had higher level statistics or math prerequisites.

~~~
dsacco
_> At UChicago CS didn't have a mandatory core of statistics or math classes_

Not even Calculus or Linear Algebra? Do they take Discrete Math?

~~~
realslimjd
Two quarters (~1 semester) of Calculus is required, so a lot of integration is
left out. Discrete Math is part of the CS curriculum, essentially as an
introduction to proofs to prepare people for Algorithms [1]. Linear Algebra is
a recommended prereq for some classes, but a lot of people don't take it
because more fundamental algebra is covered in classes like Analysis.

[1]
[http://collegecatalog.uchicago.edu/thecollege/computerscienc...](http://collegecatalog.uchicago.edu/thecollege/computerscience/)

------
hcho3
> Even newly minted Ph.D.s in machine learning and data science can make more
> than $300,000.

Wait, really? I'm just starting out as a research scientist and my pay is
nowhere that high. Am I missing something?

~~~
arcanus
They mean:

* PhD in deep learning / ML with papers at NIPS/ICML

* Working for BigCo (FAANG)

* Half of that is stock, annually.

* In the bay area or a big city

If you don't hit all those items then you pay is likely more modest. If you
are academic, it is still likely laughable.

~~~
hcho3
> Half of that is stock, annually

Ah ha. I read it as saying people getting 300k in cash. It makes more sense
now. For me, I'll have to wait out several years until my RSU gets fully
vested. Let's hope I don't get fired in the meantime :)

------
gesman
You never get what you deserve.

You get what you negotiate.

------
ashelmire
Is a Ph.D necessary for competing for those high salaries? It hasn't been for
other CS jobs, and from my experience with ML and NLP - it seems wildly
unnecessary. I'd pick experience over a degree every time.

~~~
ryanianian
Presumably the "fresh Phds" getting 300k right out of school have extremely
relevant experience and are quite capable of actually putting their learning
to use (I don't have data to prove this).

People who just know TF and couldn't implement it and know what it does and
doesn't do well aren't the kind of people attracting 7 figures.

There's a huge amount of advanced statistics, math, and "intuition" that takes
years working with data to build. Abstractions like TF (etc.etc.) make
applying existing solutions to existing problems more tractable, but the real
gold-rush is happening around the new/relatively-unsolved problems.

------
mkagenius
That’s bad and good at the same time. If more PhDs work on it, the real
capabilities and limitations will come to light leading to correction in
salaries.

------
jcadam
Most of the Data "Scientists" I've met have been total BS artists. But I work
in govt contracting, so... yeah.

------
mathperson
what is lecunn paid by FAIR? my roommate and I settled on 2-3 mil. thoughts?

------
varelse
I am amazed that rather than see this as opportunity for racking up top $$$
for _at least_ the next 3-10 years by learning Calculus,
Probability/Statistics, and Linear Algebra on the way to whatever degree one
seeks, there is so much criticism of this.

This situation is a pure win for smart people who put their minds to the task
at hand. Buzzwordy or not, AI/ML/Newfangled Regression has more than enough
wins with speech recognition, game-playing, image recognition, and
recommendations to have a strong future.

Or, if you insist on negativity and you prefer to expend your time kvetching
about whether Famous CS Person X or Famous CS Person Y should make the most
money (Fantasy Data Science?), then more for me getting $h!+ done whilst you
bicker. I mean I scratch my head that Mark Wahlberg is the highest paid actor
in Hollywood, but hey, good for him in my book. Too bad his burger shop is
crap.

~~~
wufufufu
I suspect that calculus, statistics, and linear algebra are required for most
4-year CS degrees. Some schools even have an "AI" track for their undergrad
degrees. This article specifically refers to PhDs in Machine Learning, which
is much higher bar.

If you do research in the same area that Google's research department is
throughout your graduate degree, net research internships at _insert fancy
company with AI component here_ every summer, and get a PhD, $300k seems
completely reasonable.

Personally, I think we're in a SaaS bubble and an AI bubble. "AI" isn't nearly
as developed or useful as the amount of VC money being dumped into it.

~~~
ransom1538
You could also just become a plumber and probably make $300k a year[i]
(~150$/hour). Engineers are incredibly undervalued for all the effort they put
in their education (and _containing_ education). A plumber could pull in at
least $150 per hour in the bay area. I just hate the headlines _Sky-High_
which in the rest of the professions is _normal_. Try finding an (below
average english major) attorney at $150 per hour.

[i]
[https://www.homeadvisor.com/cost/plumbing/](https://www.homeadvisor.com/cost/plumbing/)

~~~
ryanmonroe
$150/hr is the maximum of the range they give, and then you have to subtract
payroll taxes, equipment, marketing, etc. On top of that, it's not like
plumbers are working jobs back to back 40hrs a week.

~~~
ransom1538
Yes. They get to write off equipment, marketing, etc. "It's not like plumbers
are working jobs back to back 40hrs a week." In California probably more like
60hrs.

~~~
vvanders
Yup, the closer analogy is consulting but with the massive capital investment
of equipment, vehicle + professional insurance.

I'd be surprised if the take-home on $150/hr is even close to $60/hr.

------
mankash666
I wonder if the algorithms can determine a fair wage for the said talent. Like
many have observed here, the salary is for an expectation of innovation and
differentiation. However, an a-priori guarantee, or even a relatively high
likelihood of such an outcome is hard to guarantees unless the said person is
Geoffrey Hinton. So what exactly are the salaries for?

~~~
pc86
There is no such thing as a "fair" salary, especially when you're talking
about the differences between $300k, $750k, and $2 million. There is what the
market is paying similar people, what leverage you can use to negotiate higher
pay, and how much [above|below] market your employer is willing to pay to keep
you away from its competition, among other things.

The entirely idea that it's "fair" to pay someone $400k a year to do a job but
"unfair" to pay them $350k to do the same job is silly.

------
ananab
What's the best way / best resources for someone with a full-time job to learn
AI / machine learning on the side?

~~~
dbmikus
Check out online courses. Coursera, EDX, etc. Andrew Ng's intro to machine
learning course is a nice way to get up to speed with some basic concepts
without diving too far into math. It doesn't cover any state of the art
machine learning concepts, though.

Some other resources I have bookmarked:

\- Convolutional Neural Networks for Visual Recognition Youtube playlist [1]

\- Deep Learning for Self-Driving Cars [2]

\- Natural Language Processing with Deep Learning [3]

[1]:
[https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-z...](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-
zLfQRF3EO8sYv)

[2]: [https://selfdrivingcars.mit.edu/](https://selfdrivingcars.mit.edu/)

[3]:
[https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_...](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6)

