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.

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

To earn that kind of money as a plumber you would need to either work your way up over the course of roughly 15 to 20 years at a very generous plumbing company, or be an independent plumber with next level marketing skills.

The average plumber, pipe or steamfitter just coming out of their training is not going to get anywhere near 300 000 a year.

TLDR: Chance favored the prepared skill set. No fancy degree required, just results.

PS Before that, I blew a boring blind-allocated gig at Google insisting that GPUs were about to play a huge role there a year before they acquired DNNResearch. I even tried to join the very beginnings of the Google Brain team but they didn't have any openings or the budget/willingness to make one for me.

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

Are you just making up numbers? You've asked for sources, other commenters (including myself) have provided several, and the sources disagree with what you're stating spectacularly. I'm baffled by how confident you're being about something that, as far as everyone can currently tell in this thread, is plainly incorrect.

...how many plumbers do you personally know? The reason everyone in this thread is pushing back against this is because the suggestion is ludicrous. It doesn't make sense in terms of market dynamics - the barrier to becoming a plumber is significantly lower than the barrier to becoming a software engineer. That's not intended to demean the profession, it's just true - you do not necessarily need any education (and the profession embraces this far more than tech does, which is itself progressive on that point) and requisite domain knowledge is not as extensive or as rapidly changing as software engineering. You need technical knowledge, but unless you're taking the most complex plumbing jobs and the most mundane engineering work, they're simply not comparable.

Some plumbers do well, particularly if they own their own business and are thereby successful entrepreneurs. But you can't judge the typical outcome of a professional career by the entrepreneurs who use it as a basis for their business. The modal plumber doesn't earn anything resembling $300k/year - according to the BLS, there isn't a single state where the average is even $100k.[1] Where are you getting your data, from how much they're billing you when you have someone fix a problem in your house?

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1. https://www.bls.gov/ooh/construction-and-extraction/plumbers...

https://money.usnews.com/careers/best-jobs/plumber/salary

When the data disagrees with your mental model, consider re-evaluating your mental model.

The amount a client pays per hour for labor != the takehome pay of the person whose time you're ostensibly being charged for.

Revenue is not profit, and "price per hour for labor" != "hourly rate I pay my laborers". This is even true in owner-operator businesses. Furthermore, in bursty markets, "annual income amortized over career" != "my hourly rate multiplied by 40-60 multiplied by the number of weeks I want to work".

The numbers from my links are INCOME, not "what the business charged the client".

:-)

Why keep insisting on proxy variables and anecdotes?

That's the entire point of VC though - VCs don't put money into regular roads or into normal houses, as these things have already got their usefulness determined and we know they're darn useful. VCs pump money into things that look like they could use a few billion dollars to get developed and ready for the mass market. AI fits that perfectly according to your description.

VCs who put money into things that are already proven are VCs who are missing out on the Next Big Things. "AI" will become fully developed largely using the money that VC is putting into it.

My issue is that when I hear that a startup is AI-focused, that means they're just implementing something with machine learning (and possibly unnecessarily). These startups generate a lot of hype, which I believe is unwarranted.

I imagine Google researchers are working towards advancing AI techniques and knowledge more generally, not trying to disrupt some industry with existing machine learning techniques.

Any job that requires significant expertise will command salaries at the top of the curve. Corporate litigation and IP attorneys command 400k-2m salaries, yet we don't get articles about IP lawyer bubbles, because the competition is vicious.

More interesting highly paid salaries to debate:

Neurosurgeons Cardiologists Attorneys Plumbers Consultants Investment bankers Traders Product Managers

*Not in America? Yep, that's different.

(1) Calculus? I did well in courses in calculus, advanced calculus, advanced calculus for applications, general topology, modern analysis, real analysis, measure theory, functional analysis, and lots of applications to US national security and business. Taught calculus in a good university. Published peer-reviewed original research in calculus. Studied a lot more in calculus -- exterior algebra, numerical methods, the Navier Stokes equations, ordinary differential equations, deterministic optimal control, optimization, etc.

(2) Linear algebra. Worked in it for numerical methods, various approaches to curve fitting, multi-variate statistics. Did undergraduate honors paper on group representations that is just more linear algebra. Programmed a lot with linear algebra. Worked carefully through some of the best books, e.g., one by E. Nearing, student of E. Artin, and Halmos, assistant to von Neumann. Did a lot in linear algebra as part of optimization. Same for the FFT. Same for Markov processes. Reinvented k-D trees and associated cutting plane tree back tracking for nearest neighbors. First actual course in linear algebra was an "advanced, second" course from world expert R. Horn -- found very little new and led the class by wide margins on all measures. Using linear algebra and LINPACK for a small part of current startup.

(3) Crucial tools in the applications desired by AL, ML, data science need a lot in probability, stochastic processes, statistics, and optimization. Have excellent backgrounds in all of those.

(4) Ph.D. research in stochastic optimal control, a grand example of a machine doing some learning as it exploits the history of the stochastic process driving the system to be controlled.

(5) Software. Programmed in lots of languages for lots of operating systems for lots of applications, especially for US national security.

So, it looks like (1)-(5) would be good qualifications for the "shortage" of people for AI/ML?

But, I sent over 1000 resumes; my resume is on several public resume collections; I've applied to Google, Microsoft, and many others. I've never been arrested or charged with a crime other than minor traffic violations. I have not been convicted to a traffic violation in over 10 years. I've never used illegal drugs or used legal drugs illegally. I'm a native born, US citizen in the US. I have no handicaps and no serious medical problems. I've done good work in applied math and computing at GE, FedEx, and, in AI, at IBM's Watson lab.

Result: I don't get phone calls from recruiters. Basically I'm 100% totally unemployable at anything above manual work at minimum wage.

Shortage? Nonsense.

So, I'm doing my own startup based on computing and some applied math I derived. To users, my work will be just a Web site. But the site is an excellent, and the first even good, solution for a problem pressing for about 90% of everyone in the world with access to the Internet. I've designed the Web site and server farm and written the code. The code is 100,000 lines of typing based on Microsoft's .NET. The 100,000 lines have lots of comments and about 24,000 programming language statements. The code appears to run as intended and to be ready for at least early production.

Still, with that background, I'm 100% unemployable, at ANYTHING above minimum wage. This situation has cost me my chances of owning a house, getting married, having children, and my savings and inheritance.

Shortage????

I suspect it has something to do with the way you (a) look, or (b) socially interact with people. I don't mean this in an offensive way, but if you look / sound like Donald Knuth you probably won't get a job because you're not Donald Knuth. You're just some quirky eccentric person that probably won't fit in because you can't make small talk around a water fountain or you don't have an interesting story to tell after a weekend because you probably don't actually do anything on weekends that's fun. I don't know, it could be a hundred reasons. I bet it's got absolutely nothing to do with how smart you are, what you know, or who you studied under. I mean, for starters, why not just go back to academia? Are they rejecting you too?

At some point, if you're being rejected for 1000 jobs, I think you need to have an honest talk with yourself and ask whether the problem is you or whether the problem is the job market. You're a smart guy, I'm sure you can run the math and answer that question. Best of luck!

Again, once again, over again, yet again, one more time, the qualifications (1)-(5) on a resume are terrific for AL/ML and innovative applications but get no responses at all.

Lesson: The claims that there is a shortage of people with good qualifications is total BS.

So, I'm starting my own business with some crucial, core original applied math. The math is difficult to duplicate or equal, especially by people who don't value (1)-(5) above.

Each job requires someone to create it. For the special case of a company founder, he creates his own. I'm no longer hoping someone will create a job for me and, as a company founder, am creating my own.

For my Web site, it's enough for lots of people to like the site. I hope and believe that a lot of people will like the site. For the revenue, it's enough for the advertisers that my Web site delivers lots of clicks from users with good demographics, and I hope and believe that will happen. And, except for trivialities, those two are enough.

Point: Back to the "lesson" above, there's no shortage.

Why are people claiming a shortage when there isn't one? There is a standard list of reasons, and there may be reasons not on the list. I don't have information enough to select the reasons in this case.

Perhaps start by having a friend with a steady gig look at you resume? Perhaps forward it to a recruiter? Or if by the time you read this you are filthy rich, congratulations!

Moreover, the article also claims there's a pipeline for the folks (below the PhD level? The writing is a little unclear) that's coming out: " At the current education rate, an influx of new experts will start to moderate salaries in three to four years, he says."

So who is it who is making educational choices today who will be well-positioned to cash in. Probably nobody. Rather, like with all talent shortages, the people who win are those who had the luck or foresight to have already studied something that happened to get big, and now everyone else is rushing to catch up and flood the market.

Everything I did then turned out to be strongly related to techniques like Naive Bayes, Logistic Regression, Principal Component Analysis, Boosting, Bagging, and bunch of other techniques that get reinvented over and over again. Once I mapped them over to their ML incarnations, they were familiar territory going forward.

I know quite a few folks at Apple and Google without PhDs in machine learning who are now making damn good money working on ML in more research oriented capacities.

Thing is general AI, as developed by Google et al is usually only a starting point. There's a lot of engineering effort required to make a valuable solution out of it.

That said, I think it's a bit unfair to imply that the only thing required of people to participate in this particular frenzy would be to learn a little probability and statistics. I've been working on a system that learns from an enormous set of medical imaging studies for the purposes of analyzing same, and the technical ML and domain knowledge you need to bring to that party is actually pretty humbling. You're not gonna do this stuff with a little statistics and some H1B's. At least, not in a fashion that any medical practitioner will take seriously.

I think in a few years, you'll have lots of lower level people who will know enough to provide meaningful contributions. Of course, the flip side of that is that their salaries will be significantly lower.

Alternatively Dwayne Johnson as well (whom I happen to enjoy more than Wahlberg). He doesn't do high-brow entertainment (starting from his wrestling days). Simple, fun entertainment for a super broad audience never goes out of style. Your average person is pretty happy to forget their 9to5 and troubles, and go see a big movie for escapism. That will never cease to be true. Arnold Schwarzenegger filled that role when I was a kid. That's not a criticism at all, it serves just as legitimate of a purpose as people that prefer to watch TED Talks instead of the next Rock action flick. My father had a basic saying that I didn't appreciate until I was much older; whenever I would criticize something that seemed a bit neanderthal-like (so to speak), he'd respond with: it takes all types. Generically what he meant, was that the world functions courtesy of a wild variety of all types of people. Would it function better if everyone were elitist and brilliant? I don't think so.

Miranda Sings, Jenna Marbles, PewDiePie, minecraft videos, twitch game streaming, et al., same fundamental as Wahlberg and The Rock.