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Physicists taking jobs as Silicon Valley software engineers (wired.com)
200 points by electic on Jan 16, 2017 | hide | past | favorite | 173 comments

This is a pretty common career path I think once people realize that finding a well paying physics gig is much much tougher than just becoming a developer. Every physicist has at least some programming experience and are generally adept at problem solving. Coding was always way more fun than doing 4th year/grad school level physics for me too. Also, it is especially tough to get a job if you're a mediocre physicist (like me) whereas there are very many jobs available for fair to middling programmers (like me).

Don't want to guess at numbers but my gut instinct & experience tells me that very few physicists end up doing physics and most end up in finance, software, and hardware.

The American Physical Society maintains numbers on career paths for their student members, and less than 5 percent end up with a Physicist job title. Most end up in the career paths you list.

For anyone curious, here's a one year phd employment survey for physics: https://www.aip.org/sites/default/files/statistics/employmen...

803 responses, 297 in potentially full-time positions.

62% (of all physics phds employed at potentially full-time positions) in non-physics field, break down by industry:

Engineering - 20%

Computer software - 14%

Business or Finance - 11%

Other sciences - 8%

Education - 2%

Medical services - 2%

Other - 5%

The use of the phrase "potentially permanent positions" in the AIP report for industry positions, especially in the computer industry, is highly misleading. Academia, including some government labs and institutes, has tenured positions and other positions with guarantees of job security until retirement. The vast majority of industry employees are "at will" and can be laid off at any time for any reason or no reason. Senior executives often have employment contracts that take them out of the "at will" category but these rarely provide long term "permanent" status. Indeed, they can usually be fired by the Board of Directors.

The computer industry is notoriously unstable with jobs often quite short term in practice, with layoffs common.

Just to be clear, the referenced AIP report starts with the phrase:

Positions accepted by PhD degree recipients following receipt of their degrees fall into three categories: postdoctoral fellowships, potentially permanent positions and other temporary positions.

The figure on page two of the AIP report specifically lists a "private sector" block under "potentially permanent".

Tabel 1 on page 3 lists 70 percent of potentially permanent positions as "private sector." Again this is very misleading as private sector positions are potentially permanent only is the sense that there is a small chance that you might continue at the same employer until retirement; there is nothing like a guarantee.

I don't understand your strong objection to the phrase "potentially permanent position". Do you believe that the AIP is trying to mislead students into believing programming jobs in the private sector have tenure!?

To me "potentially permanent" seems like a serviceable phrase to encompass positions in academia and in industry that do not have an end data built in to them. It contrasts them to "temporary" positions like postdocs in academia or contractors in industry where you know going into the job that your employment ends on a certain date.

The phrase "potentially permanent position" is misleading. Is it intentional? I don't know.

The primary audience for a report on outcomes for Ph.D.'s in Physics one year after graduation, getting their Ph.D. degree, is students (and parents of students) evaluating whether to pursue a Ph.D.. Obviously, they should investigate longer term outcomes, but young people often don't, focusing the next step in their life/career.

Potentially permanent position sounds like a general version of "tenure track research job" as another commenter correctly noted below.

Students with little or no work experience often do not have an accurate impression of salaries, working conditions, and other aspects of the work world (or academia), specifically in the private sector computer industry where most Ph.D's in Physics currently end up.

News coverage of computer companies like Google emphasizes many far out research-like projects such as the Google self-driving car, the AlphaGo deep learning project, and so forth. These sounds like academic research, so why wouldn't these companies have comparable positions to tenure track research jobs? Indeed, in very rare cases, they may have such positions.

However, the vast majority of industry jobs are at will full time jobs without a specified end date. Especially in the computer industry, they are quite insecure and often short-lived, very different from what potentially permanent position implies. They are not analogous to tenure track jobs.

I don't think "potentially permanent position" is misleading. I can remove a permanent marker with rubbing alcohol, yet I don't say that "permanent marker" is misleading.

My job is described as permanent full time, because my contract is ongoing, and I work 40 hours a week; when I was a student, I had a permanent part time job.

This is in contrast to a casual job, which in New Zealand is similar to at-will (but with strong restrictions on what roles can be casual); or a fixed term contract.

I think "potentially permanent" is just a historical oddity. They used the same term 25 years ago when I was in school, and there was still an expectation of people going into tenure track position.

The phrase was misleading twenty-five years ago when applied to private sector positions, most of which were software development back then as well. There were few private sector positions analogous to a "tenure track" position even then, possibly a few at corporate research laboratories like HP Labs and Xerox PARC.

In any case, with the closure or heavy cutbacks at corporate research labs, AIP should update its language to reflect current realities.

FWIW, I basically interpreted it as full-time employment, but they are definitely playing with the wording a little bit and the data isn't clear on the level of employment at all.

The report is probably aimed at young people, college students in particular, investigating pursuing a Ph.D., who often are unfamiliar with the work world. "Potentially permanent positions" in the computer industry is not going to fool someone who has been working in the computer industry more than a few years. College or high school students are a different matter and won't automatically equate "potential permanent position" and "at will full time position."

"Potentially permanent positions" is a euphemism for "tenure track research job."

Yes, that is my point. There are almost no jobs in the private sector, especially the computer industry, analogous to a "tenure track research job." Possibly some corporate research labs like HP Labs, which have nearly all been downsized heavily or completely eliminated since the 1990s, have positions like this. The vast majority of Ph.D. physicists who move on to industry are getting some type of software development job which are "at will full time" jobs with no future prospect of something like tenure.

Their data is partly why I didn't pursue a PhD.

Finance use to be a big draw. Not sure if it still is. I am tempted to move to finance...

On average, a tenured physics professor will graduate a bit more than 10 PhD students over the course of their career. Physics as a field is not growing, so at most 10% of PhD students become professors. If you don't become a tenured professor there are very few jobs that aren't postdocs, and even if you wanted to you can't keep doing postdocs forever.

My first boss's boss told the story of how he got into finance from being an assistant physics professor.

For a few years, he started checking the median age of physics professors, and noticed it went up every year instead of down. Tenured profs decided to stay in their seats instead of retiring, and there were no new positions being created for young professors. That was his indication to get out (this was during the mid 90s).

The real crux of the problem is that modern PhD programs were originally designed post WW2, in the era of the GI bill and fast expansion of college education where there was high demand for new professors. That's no longer the case, but we still train PhDs as if they would get a job even though most won't.

Do you have a source for the rough number 10? I'd have guessed the average is more, even if the median is 10ish.

It is just a rough guess based on my experience as a grad student. The average professor at my university had 1-2 grad students at a time, and with 6 years to finish a PhD and about 35 years where a professor takes on grad students it works out to roughly 10. I wouldn't be surprised if the number was actually as high as 20 though, 10 is probably a low estimate. Another way to look at is that roughly half of people go on to do a 1st postdoc, half of those go on to do a 2nd and 3rd or 4th postdoc, half of those get a tenure track position and around 3/4 of people with a tenure track position actually get tenure. The problem with a career in physics these days is that a postdoc position is a terrible deal. As a graduate student you at least have job security and can stay in the same place for a good amount of time, not so as a postdoc.

It really depends on the field -- fields with labwork tend to have more PhDs per professor because there's an incentive to get cheap labor.

https://www.its.caltech.edu/~dg/crunch_art.html claims it's about 15. Though that article was written in 1993, it seems (good read in general, btw).

Still, it seems low IMHO. Unless you start counting at the point where the person gains full tenure rather than from the point where they become some kind of "group leader", "assistant professor", or such "tenure track" type position?

I think the relevant number is the fraction of PhDs that go one to a position that mints new PhDs. (That's where the multiplier comes from.) So that almost never includes non-tenure-track positions, but does include assistant professors who don't go on to make tenure.

> even if you wanted to you can't keep doing postdocs forever.

Why? I know an academic who has done exactly that. Nearing retirement now.

Many institutions have a hard rule that says you can't start a postdoc if you're more than 5 years out of your PhD program (discounting any time for parental leave, medical leaves of absence, etc.).

The usual approach in CS is that if someone wants to continue in a post-doc-esque role but has gained too much seniority, they get promoted to research scientist, or a similar title. That has higher pay but otherwise is structured fairly similarly (e.g. you can hire a research scientist on a grant as a "soft-money" position with a fixed-length contract). Some places even have additional pay grades above that, called e.g. Senior Research Scientist or Staff Scientist or Research Fellow (titles vary widely, as well as which titles are restricted to PhDs, versus open to people without PhDs). There are plenty of people who spend long periods as perma-research-scientists because they enjoy and/or are good at the position, and PIs are happy to keep hiring people who've developed a good reputation. In some of the larger departments, research scientists with a good reputation are de facto assured of ongoing employment despite the term-limited contracts, because someone who knows the ropes of this kind of job is enough in demand that if their current PI doesn't manage to snag a new grant to keep them on, someone else in the department will be happy to hire them away when their current contract expires. Besides it being useful to keep such a person around if you can afford it, some big grants, like those from DARPA, essentially require research scientists to be hired, because DARPA wants a contact who's a full-time professional researcher to interface with, not a professor / postdoc / gradstudent.

Does physics academia really not have another position you can promote someone to, if you want to keep paying them to do research for you, but they've "timed out" of the postdoc role? I had assumed this kind of research-scientist role wasn't CS-specific, since it's existed for a while, and at big universities, CS hasn't traditionally had the clout to invent completely new job titles, so hiring is usually mapped onto stuff that exists in some form or another at the institutional level.

Almost everywhere does this, and it is such an insane rule.

Not in the UK.

Postdocs at most universities pay little - often less than many engineers with a BS degree will make.

Eventually you need the money for retirement...

once people realize that finding a well paying physics gig is much much tougher than just becoming a developer.

Unfortunately, becoming a developer is a short-term optimal choice that you pay for in the long term.

The 40-year-old physicist has tenure and can work on whatever he wants. As far as his peers are concerned, he's mid-career. The 40-year-old programmer is considering plastic surgery so he can still get hired in the Valley.

The people who become developers find out that they don't have permission to get a day older than 35 unless they can make it into management. And if you were going to be a manager anyway, you might as well have gotten an MBA in your mid-20s, and then you'd be making far more than the developer would even dream of.

Coding was always way more fun than doing 4th year/grad school level physics for me too.

Programming is a lot of fun, but most software jobs aren't programming intensive. The coding is trivial and a high school student could do most of the work. The hard part is dealing with tickets, PMs, and unnecessary meetings.

Definitely agree with the coding being more fun. I found refactoring my teams messy analysis code much more satisfying than the laborious data analysis + paper writing.

For me it was also that nothing truly new was being discovered in my area of physics, so it all felt a bit boring.

Plus the politics that comes when post docs have to fight and plot over rare tenured positions when they appear.

I'm a physics phd student, but I did my undergrad in cs+physics at well known school in the midwest.

I really regret going to grad school for physics, I gave up a lot of earning potential, time to develop new skills and I've forgotten a lot of my CS background. Most methods in my area have already been developed and the pressure to publish (pushy advisor, several projects not producing the intended results, and proposal writing) has always been hampering away at my free time to think. I really dislike this system. At the same time, I'm sure exists in industry too (never had a real job). I'm not going lie, I feel a bit trapped, but I'm still a fairly positive person.

In the sciences, grad school is sold as a necessity but I'm starting to think this is a way of fueling the ivory tower.

I realize that this is a bit incoherent/ranty, but whatever. :)

You don't have to stay. Look for development jobs or internships for the Fall semester. If you like it more than what you're doing now, don't go back. If you decide that you want to get into the physics, the go back. Plenty of grad students take a semester or two off and go finish their PhDs. If your advisor blows a gasket, well, you can probably do better somewhere else anyway.

I know a lot of graduate students and early professors who have a fear of private industry. I think they all know they can make more money outside of academia, but they don't realize they can work fewer hours and not have worry about a bad tenure committee screwing up their careers. A bad boss can make my work life suck, but he'll need to work hard to completely nuke my career.

Anyway, I understand how you feel. Despite my lackluster GPA, a couple professors want me to go on for a masters in math or statistics. You should see their faces contort when I tell them I don't want to go to graduate school or I don't want a career in academia.

You spent your life in academia, surrounded by people who spent their lives in academia, who have surrounded themselves with people who spent their lives in academia. Get some fresh air, fill your bank account, and then decide.

I agree, I realized the truth of your second-to-last sentence sometime during grad school. These people have played the academia game and it worked out for them, and their sense of self-worth and prestige is tied to their standing inside the system. Of course, the academia game is a lot more difficult to play these days.

I was a Physics undergrad+ Physics / Materials Science PhD and I ended up just quitting 4 years into my PhD. You can do it too. You don't owe your advisor anything and you've definitely put in your indentured servitude. I ended up getting a job as a data analyst / scientist and never looked back. You can work on interesting problems, use the same experimental design knowledge and methods, and get paid $100k more than a grad student.

Four years? Is it common for a Physics PhD to take that long? I thought most fields are closer to two years.

Four years would be on the very short end these days. Most doctoral programs in math and the sciences are a MINIMUM of four years, with average terms creeping up to 6. Never say never, but a 2-year doctorate is pretty much unheard-of.

Some people think of this as 2 years for a Masters and 2 more for PhD, but in practice most hard science programs don't really offer a true Masters program. When I got my doctorate, a bunch of us figured out we could apply to get our MS diploma after we finished our coursework -- hardly anyone in the PhD program had ever bothered before. We all got them and put them on our desks as a kind of inside joke.

Don't some universities give MS diplomas out to people who make it so far in the program, but drop out before getting their PhD as a kind of "well, here's something for your work"?

Yeah those are usually referred to as "terminal masters", meaning that you terminated your education with as MS.

"terminal masters" more usually refers to a field where a Masters degree is the highest attainable degree, like a Masters of Fine Art (there is no Doctorate of Fine Art).

Although searching Google - I can find references to the usage you're giving.

But, I fully agree that a Masters in a hard science, especially from a top research school, is almost always a consolation prize for those who drop out. My program would not accept students into the program for a Masters, it was PhD only, unless you dropped out.

That's been true for awhile. After I graduated in the late '80s I wanted to go back to school for a masters degree and was politely informed by multiple universities they were only looking for students who intended to get a PhD.

Ah I must have been mixing up Masters and PhD.

Stats for European schools can also be different. I know of some that have short PhD programs but require you complete a masters before applying.

I took 7 years from the start of grad school to my PhD. The main reason was that my first thesis project was an utter failure.

My impression from talking to a lot of people, is that the European schools simply manage the PhD process to make sure it doesn't drag out too long. It's expected to be shorter, so it's shorter.

My best friend and I both did PhDs in astronomy starting at the same time, me in the UK, him in the US. I completed in 4 years, he took 9 years.

The big difference was that the PhD in the UK launched you straight into research on day 1. No teaching, no courses. He spent the first couple of years in courses, and a lot of time throughout the degree in teaching. In some ways I envied his coursework, since I was basically on my own from the start (his grad courses sounded very thorough and interesting). However I do not envy the 5 extra years it took him.

That was all a couple of decades ago now, so things may have changed.

Indeed, my graduate coursework was full time for 2 years, and the failed thesis project probably cost me 3 years.

Luckily, AIP has a survey for this as well!


Only 3% of surveyed physics PhDs took 4 years, nobody took less and 6-7 years was average.

My Ph.D. took 9 years from start of M.S. program til defense.

I was, relatively, fast at it as well. M.S. was 2 years. Ph.D. was 4 years of research, 3 years of writing. Though I spent the last 2 years of writing ABD working full time for a "startup".

I really enjoyed my time in grad school. I was a computational physicist, doing molecular dynamics. The reason I went into academia was lack of professor positions . The writing was on the wall. So I decided to go the industrial route.

With the impending closure of my latest venture, I am looking around at options, and am actively interviewing. As it turns out, it looks like my thesis advisor is retiring at my alma mater, and they have an open position.

I am thinking deeply as to whether or not I really want to do physics. The pay is terrible, and I am marked as an "undesirable" as I had left academia.

I'll keep looking for my next place. This said, the Ph.D. was a good thing to do. I learned a tremendous amount, not just about a narrow area, but how to compute, measure, and reason about things that are complex.

Seems like this is in demand now. Makes me happy.

People who go straight from undergrad to PhD often take 5+ years (in the states and Canada, at least), especially in the experimental fields

For many in experimental tracks, when I was in graduate school for a physics PhD, 4 years seemed like the shortest you could get out in. There were a lot of people that started immediately at 22 and were almost 30 by the time they were ready to graduate.

If you go straight from undergrad into a PhD track four years is a reasonable minimum, yes. You can at some institutions at least opt to take an MS along the way (as I did) after your two years of graduate level course work is completed, and then continue to complete the PhD.

I was 6 months into my PhD in physics - just been given assignment based on obscure Austrian codebase and a quick briefing based on nature article ("Let's discuss when you have something - seeya!") when an opportunity in software came about (a graphics gig - the last thing I wanted to do was write CRUD apps). Never looked back. I was ridiculously underequipped in theoretical as well as practical side beyond linear algebra and C++ but we had a great team and I had the chance to learn quickly.

One thing - if you decide to move to industry, make sure the first years you can learn from someone more talented than you - even if informally. This is critical, software is a craft.

A few points.

I spent 7 years in grad school, then quit without a PhD. I don't regret it for a minute. I managed to spend 7 years studying topics that interest me, that I will not get to study had I gone straight into industry. Sure, while working you may have some spare time to study some math/physics. But nowhere near at the level you could/did in grad school. There's really no comparison.

If you're not happy in grad school, perhaps:

1. You don't like your research topic.

2. You don't like your advisor.

As an example, I did not have a very pushy advisor, so I learned what I did in a very relaxed manner. Bad idea if I were to end up in academia as a career, but it worked out great for me.

It does suck not finishing the PhD, but once I was in industry, I saw the jobs that most PhDs in my research topic would have ended up with - and most of those jobs are horrible. I worked with them for 4 years, and moved into programming. While the satisfaction of solving really challenging problems is no longer there, the programming job overall really is much better: More autonomy, more creativity, better work schedule, etc.

But bottom line: Grad school is for learning the stuff you are passionate about. If you're not doing that, either change your topic/advisor, or leave.

Don't feel trapped. There are lots of CS experts out there who are competent but have little domain knowledge expertise and therefor limited understanding of what they're writing software for. Just push ahead and see it through until you get bored with it. Being the person around who can write software and understand the underlying physics of this or that problem that you're seeking to model makes a world of difference and is a very valued skill in some fields.

We hire people with your exact profile for data science jobs all the time. You should be applying to DS jobs like there's no tomorrow.

Also a physics PhD student. I never touched CS as un undergrad, but I got very interested in writing software as an undergrad when I started in an experimental HEP group (I've never taken any formal programming courses). I don't completely regret going to grad school even though I often think about the likelihood that I leave physics when I graduate to get a job in software development. I also dislike the academic system - and that's the biggest contributor to that likelihood to leave for the software industry being high. The physics itself is no longer _incredibly_ interesting to me, but I can't shake the data analysis itch ;)

> Most methods in my area have already been developed and the pressure to s/publish/ship (pushy s/advisor/PM, several s/projects/products not producing the intended s/results/sales, and s/proposal/budgets writing) has always been hampering away at my free time to think. I really dislike this system.

Sounds like you have plenty of "real job" experience ;-)

While being a student a physics professor told me:

"Study physics now. Because later when you have a job, you might get paid to learn using some software tool or a new programming language. But no company will ever send you into a quantum mechanics lecture. "

He was right.

Oddly enough, I received similar advice from my parents, 35 years ago when I started college. This was of course just at the dawn of the personal computer age. My mom was teaching programming at a nearby college, and she literally thought that programming was too easy to spend 4 years learning in the classroom. People were getting programming jobs after one year in her course. Also, nobody had any idea where the industry, or the economy, were headed.

Both of my parents started their careers as scientists in industry after getting graduate degrees in the 50s. They saw people with science background being able to go into practically anything, including programming, business, entrepreneurship, and so forth.

I learned programming in high school, and fell in love with it, but I had an internship at a computer facility, and also looked ahead at the typical CS curriculum. It all seemed terribly boring. So I majored in math and physics. Oddly enough, the people who were doing things with computers, that interested me, were in the physics department. I developed the ability to design computerized electronics for measurement and control systems -- which became my career. This was even before "embedded systems" was widely taught in EE departments.

But realistically, a large portion of the software industry today does not require people with a science background. What I don't know is if I'd still find it boring.

Perhaps my parents' attitude was along the line of "you can do anything with a liberal arts education," but with the stipulation that the liberal arts include math and science.

> no company will ever send you into a quantum mechanics lecture

I bet D-Wave has some interesting quantum mechanics lectures!

If you make enough money, you can send yourself.

No he wasn't. At the google X labs we had a quantum mechanics lecture from a guest speaker, literally only a couple of months back.

1 lecture at arguably undergrad level vs. 30 lectures (2 semesters) of grad level qm

This migration of physicists to the Silicon Valley and computer industry is not new. It has been true at least since the first big physics employment bubble crashed in the late 1960's, early 1970's. The post-Sputnik boom in physics degrees and grad students produced a huge surplus of physicists by the late 1960's. Dennis Ritchie started at Bell Labs in 1967. Back in the 60's, 70's, early 80's a fair number of physicists decamped for Bell Labs, mostly to work on computer and telecommunications related activities.

A high profile example is Emanuel Derman, author of My Life as a Quant (2004) and later books, who worked at Bell Labs from 1980 to 1985 before moving on to Wall Street. He mentions quite a number of other physicists at Bell Labs at the same time.

Most physicists end up in some sort of software development. The high profile "quant" jobs are actually rather rare and hard to get. The Wall Street firms are typically going after very strong physicists, especially theoretical physicists like Derman.

Nathan Myhrvold of Microsoft and Intellectual Ventures fame (or infamy) has a Ph.D. in theoretical physics from Princeton. Did not go to Wall Street. :-)

The Large Hadron Collider (LHC) produced a huge surplus of experimental particle physics (high energy physics) Ph.D.'s with no jobs in the field. Experimental particle physics involves large amounts of software development for data acquisition, instrument monitoring and control, and data analysis, mostly in C and C++, although there is still some "legacy" FORTRAN software. The heyday of FORTRAN in physics was a long time ago.

Although there have been attempts to use neural networks and other machine learning methods in particle physics, the workhorse of data analysis in the field is Ronald Fisher's maximum likelihood estimation and classification -- primarily estimation of parameters such as the mass and width of the Higgs Boson. The discovery of the Higgs was a maximum likelihood analysis.

Although it is undoubtedly possible to map maximum likelihood onto neural networks, in practice they are different. Neural networks are an attempt to simulate the low level structure of the neurons in the brain and solve problems by brute force fitting of data to models with huge numbers of adjustable parameters. In contrast, maximum likelihood involves attempts to understand the phenomenon under study and model it as a small number of functions corresponding to higher level concepts such as the Higgs Boson. A neural net could exactly fit the Higgs Boson peak yet never produce or confirm a physical model of what causes the peak.

This is a good summary, but we used both machine learning and maximum likelihood to discover the Higgs Boson. The difference is that we frequently use machine learning to identify the (already discovered) particles that the Higgs decays to.

Part of that is because there was only physics and math degrees up to around the 60s. There wasn't really EE or CS, and even when there was, most the professors were originally trained as physicists.

There were definitely many EE degrees. Electrical technology dates back to the telegraph, telephones, lights, early electric power in the 1800's followed by radio in the 1920's. Richard Feynman started out as a EE at MIT in 1934 and was one of only a small number of students who got a Physics degree in his class. Most MIT students got EE and other engineering degrees. EE was hot in the 1930's It is true that CS is a new field in the 1960's and 1970's.

>The heyday of FORTRAN in physics was a long time ago.

Ahh, how I wish that were true...

Physicists have been taking jobs as software engineers since the 90s. Career-wise, physics is the comparative literature of the exact sciences.

/me raises hand

Degree in physics, working software for 20 years and still feel like an impostor ... not that I remember any physics at this stage

In 1997, I met my former professor of Quantum Mechanics and he said, "I hear you've gone over to the Dark Side". He was referring to the start of my professional software development career. I'd been an amateur developer for a decade prior to that, but took a break to pursue a degree in physics.

We had a long discussion during which I told him that I'd always wanted to program computers, but didn't think it made sense to get a degree doing something I could learn in my bedroom. Physics, on the other hand, was fascinating, and could only be truly learnt and appreciated from people who'd devoted their lives to it.

20 years later, I think those years of training in physics have made me a better developer, and taught me to better see patterns in data. To prefer "good enough for the task at hand and elegant enough to be proud of" over "perfect" (which is what many of my math friends ended up seeking).

Impostor syndrome seems bogus but only because I've never experienced it and therefore can't understand it. Why do you feel like an impostor? The world is analog, not digital. There's no rite of passage that will suddenly switch you from being an "impostor" to being "legitimate". I've met EE majors who turned out to be better software developers than CS majors and vice versa.

First, a tip -- if you genuinely want to learn more about something, don't start by saying it "seems bogus". Not the best way to open up a discussion.

On to your question: It's hard to pin down the exact root cause, but it seems (in my case at least) to stem from being an outsider -- a y in a sea of x's. When everyone else doing your job has a specific attribute that you don't, you can start to wonder whether you really belong in that role. The literal feeling is, "One of these days, I'll say the wrong acronym in stand-up, the rest of the team is going to figure out that I'm just winging this Agile Scrum thing, and that will be that."

That can mean being the only physicist in a company of CS grads, or the only woman on an all-male engineering team.

Being in a job where you're always learning, received no formal training, and where the expectations are fluid can amplify the feeling, since there's no yardstick against which to compare your performance. If you're a fast learner, it can be hard to believe you've gotten as good at something as people who had years of training/have been doing it for years.

But, see, for every single person in the standup, there's something. "If they figure out that I'm not as smart", or "that I don't really understand Android", or Java, or Eclipse, or databases, or that I went to a lesser school, or...

Here's a group of people. Compared to each one of them, I know less about something. It's really easy to go from that to feeling like I know less about what I'm supposed to be doing than everyone else.

I don't really experience this, I suspect because of arrogance. I'm not sure that's really an improvement, though...

Yes, of course -- that's why it's called Impostor SYNDROME. If you think about it logically, of course you belong there, and everyone has their own foibles.

Someone's perception of their abilities is (usually) not the same as their actual abilities—that is, some people will over/underestimate themselves, which manifests as overconfidence or low self esteem. Sometimes its in a specific area too, like one person who thinks they're god's gift to programming (when they actually cause more problems than they think) but also hold the simultaneous belief that they're unattractive and un-datable to the opposite sex (the reality is somewhere in between).

> I can't understand something so it must not be real

Maybe that's a problem with you more than reality.

> I can't understand something so it must not be real

I never said that, don't twist my words. I said it seems bogus because I've never experienced it and I don't understand it. I never said it isn't real - it probably is; I just have a hard time relating to it and accepting it.

You do know what the word "bogus" means?

You do know what the word "seems" means?

Speaking personally, I used to experience alot of impostor syndrome type feelings. After doing alot of work with therapists around family of origin issues, I don't experience it anymore. I can understand if you don't get it, for me it was an issue of misperception that took correcting at the root.

> The world is analog, not digital.

While true as far as our current mathematical models are concerned, there are good reasons to expect that deep down the world's fabric is discrete. (Whether that counts as being "digital" is probably just a matter of language.)

>there are good reasons to expect that deep down the world's fabric is discrete

There are no reasons to expect one way or the other. As far as we can measure, spacetime is discrete. However we can't probe very far in the grand scheme of things. Without experimental evidence, and without a particularly strong argument one way or the other, it's useless to be speculating.

> As far as we can measure, spacetime is discrete.

Did you mean to say "continuous"?

Quoting Wikipedia [1], "physical spacetime is expected to be quantum because physical coordinates are already slightly noncommutative."

[1] https://en.wikipedia.org/wiki/Quantum_spacetime

Since the '40s, as the article notes. Early on, science/engineering/math problems were one of the driving forces of computing, typically with a distinct separation from commercial/administrative side — for instance, scientific machines had floating point while commercial machines had decimal arithmetic. It wasn't until the First Dotcom Bubble that the commercial culture decisively ‘won’.

Don't forget about Dennis Richie, the creator of C programming, according to Wikipedia he holds a degree in Physics and Applied Maths[1].

1- https://en.wikipedia.org/wiki/Dennis_Ritchie

There was a large cohort of early computer science luminaries, Edsger W. Dijkstra among them, who schooled for physics and went to CS when there was a glut of physicists after the end of WW2.

I was ahead of the curve - I did it in 1985. Never used any of the physics, either...

... until 20 years into my career, when I got hired by that medical instruments place that was doing 3D reconstructions of patient anatomy from a series of 2D X-rays. Then it was 3D coordinate transforms, Fourier transforms, some other calculus, plus radiation physics.

A lot of stuff you learn isn't relevant to your career - until it is.

I know a self-taught developer who was specifically getting a degree in physics because of the wide variety of fields that it could allow him to work in. If you insist on getting a degree, degrees in engineering fundamentals like math or physics are probably the best things to target.

I disagree. In my experience (and talking with classmates) finding a job as a generalist (physics/math) is much tougher than specializing in CS, finance, engineering etc. Yes, there are more paths to take, but there are generally large hills on those paths that need to be overcome first...

I don't have first-hand experience as a candidate so I can't really contribute more to the discussion. I assume that like most positions where the job isn't a 1:1 mapping with the degree, some initiative beyond just presenting the degree will be necessary and you'll need to demonstrate an awareness and competence in the field you're pursuing.

I've done a fair amount of hiring and I know that a candidate with a physics or mathematics degree who also had a programming background that was commensurate with the position would look a lot better to me than one with a compsci degree, all else being equal.

That should tell you what college degrees are worth these days, though. The candidate primarily needs commensurate background. Degrees are effectively neutral -- too widespread to penalize people for taking that route, but really not worth much extra, unless in the course of your studies you accidentally did something really interesting, but then you probably could've done interesting things without the educational institution.

Anderson left Harvard before getting his PhD because he came to view the field much as Boykin does—as an intellectual pursuit of diminishing returns. But that’s not the case on the internet. “Implicit in ‘the internet’ is the scope, the coverage of it,” Anderson says. “It makes opportunities are much greater, but it also enriches the challenge space, the problem space. There is intellectual upside.”

Was thinking of pursuing masters/PhD in economics but have heard/read a lot along the same lines of an intellectual pursuit with diminishing returns both in terms of salary and real world impact.

The most useful thing I've done on Twitter is follow a bunch of economists and professors who are constantly debating current econ thinking/papers. There are a few I really respect, but for the most part both the academic environment for PhD level economists seems kind of toxic to me as an outsider.

Been thinking a lot recently about how my generation (I'm 24) is going to have to be a lot more practical due to economic realities...

I did a master's in econometrics and that was my experience. In the last year of my undergrad, I took a course in machine learning that seemed to have way more intellectual rigor than any of the material covered in grad school.

At one point, I mentioned to the professor that I was concerned that the model he had presented was overfitting, and he had no understanding of what the term meant. I think that economics studies fascinating problems (how do people make choices? What are the optimal choices for policy makers to make?) but economists approach the problem completely wrong.

With deep learning overfitting has been thrown out of the window..

Obviously overfitting is always a concern, but deep learning has (when done correctly) found ways to mitigate concerns about overfitting.

Dropout, for instance, or k-fold cross validation.

Interesting.. would you suggest some to follow? And what is the current consensus/debate?

We are 20 years out of NAFTA and free trade with China. I see a lot of journeymen/trades that do well in union gigs, police/fire/teachers and blue collar professionals... Those who aren't doing well are people outside of this (service/retail) and illegal immigrants. I guess manufacturing was a union gig that one could get into... On a side note.. why does a Tahoe cost 70k... I mean I know people pay that but seriously... unreal. A Tesla too.. crazy expensive.

Noah Smith tries to describe the overall state of macro in this recent post: https://www.bloomberg.com/view/articles/2017-01-11/tribal-wa...

He starts out strong and gives a good overview of the "schools" that have been laid to rest, but concludes on some kind of utopian, happy note that I don't believe he has any evidence to support. But then again he spends most of his time in conversation with leading economists (I think) so he should have a pretty good idea of what's up.

Paul Romer has a good summary of the state of macro in his 9/16 paper "The Trouble With Macroeconomics". Here's the abstract:

For more than three decades, macroeconomics has gone backwards. The treatment of identification now is no more credible than in the early 1970s but escapes challenge because it is so much more opaque. Macroeconomic theorists dismiss mere facts by feigning an obtuse ignorance about such simple assertions as "tight monetary policy can cause a recession." Their models attribute fluctuations in aggregate variables to imaginary causal forces that are not influenced by the action that any person takes. A parallel with string theory from physics hints at a general failure mode of science that is triggered when respect for highly regarded leaders evolves into a deference to authority that displaces objective fact from its position as the ultimate determinant of scientific truth.

It's a good one...https://paulromer.net/wp-content/uploads/2016/09/WP-Trouble....

Personally, just from reading books and papers it seems like there are very few economic clans left, just economic celebrities (Krugman, Varoufakis, etc.) and the profession's credibility is suffering due to the Fed (and other leading central banks) inability to make anything happen with an empty clip monetary policy wise.

The Economics job market has actually been much better than the Physics or Math job market. Marketing and Accounting are good too.

Who are the Econ people you're talking about on twitter?

You've surely run across them, but: Brad DeLong and and Mark Thoma post a ton of interesting links every day.

Yup, both good sources of econ info, thanks for the share.

Whoa! I am actually currently in a physics PhD program, in my last 1-2 years, and have been pursuing internship opportunities for the summer. For me, I've actually wanted to get my PhD in physics and then work in tech for as long as I can remember (so it's not primarily that I feel like the academic job market is too competitive for me, or doesn't pay enough, but those are pretty good reasons, too...) .

I actually have often found it awkward to fit into the usual computer science bins that companies organize projects into. In addition to a hefty amount of data analysis and software engineering, my PhD has required knowledge from a wide variety of different types of engineering, including optics, microwave engineering, and semiconductor fabrication. Combining everything into a 1-page resume, I've found it's not super obvious where my placement would be within most companies. I am currently in the team-matching phase for a google internship, for example, but haven't heard anything.

For those that are getting themselves in the door, do you have any tips?

Craft your resume around the job you want. Add in the other details to fit. If you dont know what you want, do more internships, soul searching, or perhaps even explore career coaches.

The best thing getting my food in the door in silicon valley was to move there; quite a few opportunities for qualified candidates afterwards. Even research labs at Stanford or Cal still value hiring the broad experiences you describe and could be a foot in the door.

> For those that are getting themselves in the door, do you have any tips?

I have no tips, but a guide to dealing with recruiters (who are typically pattern matching for the framework du jour) for developers from a (hard) science background would be really cool!

Have you thought about de-emphasizing the less relevant experiences? Find the companies you're interested in, tailor your resumé to be mostly what is applicable, and then hint at your expanded experience. As an example, a one-liner on "optics, microwave engineering, and semiconductor fabrication" says a lot already.

Send me an email (my username at google.com). We may be hiring an intern this summer for a physics project.

Yes, Google does interesting science research as well as hiring physicists, though we have plenty of those, too. I'm one of four on my team.

Wow, too much data science/ machine learning hype into one article. If you are a Physicist that can make machine learning models doesn't that make you a coder also? Furthermore, they give two examples who weren't Computer Science students but I'm sure I can find a counterexample to this.

Physicists are one hell of science and engineering badasses.

I know many physicists who became hardware engineers and even software engineers when they needed it. I know no software/hardware engineer who became physicist.

Depending on what "being a physicist" means, I did. Went from EE undergrad to physics PhD. Then worked in s/w but still tinker on physics projects on the side.

GRTensorIII (https://gitlab.com/grtensor/grtensor/wikis/home)

An iOS/Android app for Three Body simulations (Three Body) and a Unity gravity simulator (Gravity Engine).

I would have loved to make a living wage in physics but the post-doc path is badly paid.

You have not spent couple of tens of years building EE career and then turn to physics. I am speaking of fully developed specialists in the field - well into their thirties and even forties.

I'd become a physicist if there were jobs for physicists doing actual physics.

Me (and you) can do theoretical work.

Even nanomanipulators are DIY: http://www.popsci.com/diy/article/2011-07/you-built-what-sca... or http://hackaday.com/2015/01/13/cheap-diy-microscope-sees-ind...

(ESM can be used to manipulate atoms)

Yet I haven't had time or courage. Or badassness.

To work on theoretical physics, you anyway do need a job in physics or win the lottery - doing it as a hobby for 15 hours a week after a job doesn't count, to get anywhere you do need to be able to work on it full time for a few years.

And this is what I call lack of courage.

For some, that's because they're not badass enough.

For many, it's because they never needed it.

"I know no software/hardware engineer who became physicist."

Sure, due to the premise of the article; no pay in it.

Even wealthy ones can't.

John Carmack was wealthy enough to afford spaceship company.

This is closest one get to physicist. And yet - no quantum or particle physics, theoretical or experimental.

Were he alive, John Bardeen would want to have a word with you.

He was not a career hardware engineer turning to be physicist. He was physicist from the beginning.

Does anyone else think that general programming education (not necessarily computer science) is overrated?

I much rather hire people that I know can think in both in terms of fundamental logical principles, and who understand the scientific method, which comes up more than one expects in business. For example, I've seen physicists run much better marketing analytics than "growth hackers".

And who cares if someone can cross-off a list of generic programming language/frameworks? If you need that specific of a cog in your machine, the position probably isn't that innovative and you're better off outsourcing. Or hire someone smart and eager, pay them decently, and they'll learn what they need, if they're so inclined.

I would agree. I'd much rather deal with people who are good at math or know something about a particular industry. If you are a little disciplined and accept that there are some rules to follow learning software development is not that hard.

How would you recommend searching for those sorts of roles that aren't just cog-in-a-machine but require a good bit of technical ability and creativity on the job? No offense, but if you look at most job listings (even on hacker news), they often specifically mention technologies and languages one should already have a background in, and it's definitely not clear they would take someone that has succeeded in differing technical challenges.

A lot of employers will list too much stuff just because it's an ingrained habit. In my experience, both as an employee and employer who used to do this to some extent, these requirement lists are fairly 'negotiable'.

If you have the top 2-3 things they'd need (e.g. CS algorithms knowledge + some scripting language + some database experience), and are otherwise a good applicant, go ahead and apply with the mindset being that you'd have no problem picking up other frameworks X,Y,Z. Of course, spend an evening to take a look at them and be confident enough to make that the case.

I don't get it.

I'm studying physics because I want to help shed light on the mysteries of reality.

It might be hard to get a job in physics and achieve that, but it's a hell of a lot harder to get in a job in software development and achieve that.

Despite this, all I'm reading is optimism about how well-paying and interesting software development is for physicists. So what? If those were my primary concerns, I wouldn't have studied physics in the first place!

I get the hidden impression that the meaningfulness of science -- pursuing the truth, the nature of the universe -- is being swept under the rug because it's no longer paying the bills. That's a goddamn tragedy, not the cause for celebration this article is making it out to be.

Don't give up. It is possible to have a career in physics. It's not the easy path: academia has its many challenges, and software is in it's glory days. But the Universe is still there, and there is still so much to learn.

There was a time before computer science degrees existed. Guess who were some the first folks to use computers and pioneer our field? Physicists and mathematicians!

Von Neumann, Feynman, Metropolis, Vapnik, Babagge, Lovelace, Turing, Church, Hopper, Brooks...

Anecdata: As a software engineer who studied physics before C.S., it feels like I've had a massively unfair advantage my entire career. Served me very well.

Can you describe the advantage?

(Was within a hair of a physics minor before I studied C.S., but might have wiped out the advantage by switching to Mathematics?)

Most of the CS students don't like math, so CS teaching has developed a tradition of only the minimal need-to-know math exposure. But then you have to always operate at the very edge of your math competence, because that is all you were taught. If you have a solid math foundation, then most of the math concepts you face in CS are well within your comfort zone.

No real "news" here.

AIP does many nice studies of where physics PhD's end up.

One of the hardest challenge as a Physics PhD in industry is finding interesting problems. See fig 2.2 for reporting on that: https://www.aip.org/sites/default/files/statistics/phd-plus-...

I'm working on a Masters in CS right now and have been interested in heading into some form of 'Data Science', whatever that may entail.

Does this mean that only physics/math/stats majors are getting into this field? At my current place of employment this is the case for the data scientists; all have an academic background in one of these three fields, all have a Masters or PhD. I assumed this was because the head data scientist had a long history with academia and thus had a predilection for academics.

From my experience the data science concepts I have encountered thus-far seem pretty straightforward and I suspect that people are trying to make these concepts seem more difficult/arcane than they are by using notations/concepts only learned in academia. Am I wrong here? Is it actually a field which requires a PhD in Math to understand? My exposure has only been with toy examples (logistic regression, simple perceptrons, similarity algorithms, etc). How easy is it to get into the field without a heavy academic background?

move over javascript, matlab will soon rule silicon valley!

edit: this is just a tongue in cheek comment that physicists will rule silicon valley with about the same chance of success matlab will be used for enterprise dev.

you must be thinking mechanical engineers. Real Physicists use fortran.

Pencil and paper is where it's at.

Move over matlab, numpy will soon rule data science!

Javascript will use Numpy and then both will rule data science.

Doesn't really seem like the author spoke to a lot of actual physicists to write this article. Don't get me wrong, physics majors attract a certain type of intellect, but the vast majority of curriculum (quantum, EM, mechanics) are things THAT HAVE BARELY CHANGED IN THE PAST 50 YEARS. Meanwhile, CS majors come out much more prepared and hirable on the job market.

As far as the machine learning market goes, 90% of the projects require software engineering skills, the last 10% requires being able to go underneath the covers of linear algebra libraries, etc.

I just think the whole physics>cs degree for machine learning argument is not totally persuasive given my experience.

They have to turn it into some kind of story with more excitement and conflict than "some people who are good at math are getting jobs as engineers".

How is the notion that "the vast majority of curriculum (quantum, EM, mechanics) are things THAT HAVE BARELY CHANGED IN THE PAST 50 YEARS" relevant to this discussion?

Because the author draws a lot of parallels between computer science majors and physics majors, citing that physics majors are better prepared for the type of work ML requires. I am a physics major turned data scientist, and my argument is that I would have better prepared having been a CS major, given what the majority of my work requires. While in a CS major, a lot of data structures and algorithms haven't changed in 50 years either, you're much more likely to take electives with marketable skills or with up-to-date technologies (distributed systems, operating systems, OO/fp, databases, concurrency) that would've helped me in my day to day more than a math course or too did during my physics degree.

Considering how competitive physics graduate programs are at top schools it is a shame to hear that so few are pursuing careers in physics and some appear to be calculating in advance their move to CS. I love CS but if I had a passion for physics I would be pissed to see someone that society, schools, mentors, peers have invested in to do physics go be quant at an investment bank. It is not that working for a bank is intrinsically bad, more the case that individuals should consider the opportunity they may have been taking from others in pursuing a specialized education they subsequently don't use.

I myself have only a CS background (BSc + MSc in CS), and I am working in IT for last 8 years. Initially, I used to have negative opinion towards people from other non-CS backgrounds able to get jobs as Software Engineers and as IT Managers/Directors.

Now for the past 5 years, I am highly appreciative of such people because each of them bring knowledge and interesting viewpoints from their respective academic/training backgrounds which serves to enrich the IT world. Thank you guys :-)

> In other words, all the physicists pushing into the realm of the Silicon Valley engineer is a sign of a much bigger change to come.

So this article comes all the way to this. Is there data that hiring of physicists speeded up in the recent years? And why roles they are taken? Presumably not all in machine learning, if I have to guess.

Until then, this is another effort to turn an otherwise good interview into an unsubstantiated editorial aiming for nothing but hype.

Most physicists who recently graduated who I know (who don't do a postdoc) have transitioned or are looking to transition into data science. I am in the looking to transition group, so my sample may be biased though.

This starts pretty early. I mean, would you rather be treated like dirt doing your prof's work for her or him for poverty-level wages (if any!) or making a decent income as a CS intern?

I got my undergrad in physics, but spent all of my free time (and a lot of what probably should have been study-time) teaching myself to code. It had a pretty brutal impact on my GPA, but I've no regrets.

Nitpick: newton's method is intractable in Deep learning since it's computationally intractable to compute the hessian matrix

Newton's method might be, but quasi-Newton methods like L-BFGS[1] are usable if you can calculate the gradient of your problem and have enough memory to store several previous coordinates and gradients (so maybe 10x the memory requirement of gradient descent).

[1] https://en.wikipedia.org/wiki/Limited-memory_BFGS

Yep this was used by Jeff Dean et al. in one of Google Brain's earlier experiments https://research.google.com/archive/large_deep_networks_nips...

Mathematicians as well. It's not just physicists. In fact any of the STEM fields is a valid path to software engineering.

I was a CS student who (personally, not a critique of all CS teaching) got tired of the way CS was taught at my school, which focused so much more on algorithms (which is good) and how to implement them (which can also be good) but tested the knowledge in ways I thought were only semi-useful. Fractions of a second of performance were more important to your grade than full correctness and good style. Collaboration was highly discouraged, which to some degree makes sense, but led to students who were inevitably terrible at group projects in senior year.

This eventually drove me to take a year off and then resume as a Physics major. I'm almost done now with my BS and am so happy I made this choice.

Despite switching to a Physics major, I'll probably end up working in a programming position eventually. It's a far easier career to get into.

Physics major here, but programming since age 11. I wasn't a fan of how CS was taught to my friends, but undeniably there is so much incredibly important knowledge in CS that you won't get in a Physics degree. Such important works as the Dragon compiler book, Introductions to Algorithms, SICP, the Purity Test. Since coursera came along, I've found a number of online courses absolutely mind-altering. Basically, to my fellow physicists: yes, you are a smarty pants, but respect the body of CS knowledge and learn it.

Is there a huge difference between physics PhDs and computer science PhDs in this argument? Both are overspecialized in their research, but both also have a wide range of skills in programming, abstract problem solving, writing, communication, and so on. Both will likely have high-level knowledge of a variety of machine learning methods, except the CS PhD that actually published machine learning research who will know a bit more about some very specific topics.

(I'm a CS PhD and I know many physics PhDs as well.)

Strange how the article doesn't mention that (not Hinton), Hopfield, Sejnowski, Kohonen , Amari etc. etc. were all phycisists.

Also, physicists working for software is not only a silicon-valley phenomenon .

Hinton was never a physicist.

I sat right next to a really smart guy who was a hadoop administrator.

Turns out he got a PhD in theoretical physics and did some graduate work at Fermilab.

Holy crap...this run of the mill Hadoop administrator is some kind of physics genius.

I asked him why....he said that his true love, particle physics, just didn't pay the bills like any random IT related job does.

Which is a damn shame.

PS: While we were talking, a dude one row over popped up and said he had a PhD in Chemistry or something.

Can confirm, have a physics PHD friend who is now doing primarily programming work.

I, for one, welcome our new physicist overlords!

overlord is a strong word. As a physics major, I would prefer to be called a tech minion.

Aerospace engineer here!

Not a physicist and not that there's a lack of open jobs, but I was also way more interested in programming.

I started my own project in 2nd year of university and it changed everything to me. I've been able to apply my knowledge during my years of university and also out of it with my side-projects.

Same as it ever was.

All my best developers over the past 25 years I've managed were either physicists or mathematicians.

Did physics undergrad, worked as a developer for a couple of years, then did a computational physics PhD, now firmly back as a developer.

I would do open ended research if there were any jobs going, but there generally aren't. And I'd be just as happy in computer science as physics.

I didn't study CS, I more use software (like django) than build it, and, in fact, probably misuse it. But the few plasma physics classes I took for my MS are an order of magnitude more complex than any programming I have ever done.

its really great that our world's top scientists are working on ad monetization

I'm surprised that this is surprising.

Physics as a career doesn't pay terribly well, but as a degree offers a very wide breadth in coursework and skills. Grad students in particular usually have experience in HPC, for instance.

I studied physics, with intention of becoming a developer (which I did). I didn't have much appetite for a CS course and felt the physics would give me a broader set of skills.

I followed a similar path and can only agree on the attitude. Most of the relevant CS knowledge is just that: knowledge. Studying physics is usually less about knowledge and more about general problem solving skills.

I'd go beyond "general problem-solving skills". Physics gives you high quality math training (albeit somewhat truncated with respect to number theory), coupled with demanding experimental methods, and a deep, principled understanding of the foundations of the universe. Nothing is beyond your reach. Any other quantitative or logical discipline is just laying there, waiting for you.

The effort one has to put into getting a physics degree seems to me comparable to that of becoming a physician (a medical doctor), and it pains me to see all that effort wasted. Some argue that it is the acquired skill in solving problems that matters, but, having seen physicists and mathematicians (and even "computer scientists") by training trying to solve software engineering problems, I have to disagree. Likewise, from what I have also observed, problem solving skills of a good software engineer may not be by themselves all that useful when trying to tackle problems in physics and mathematics.

Not a new phenomenon. Most super-programmers and computer scientists (especially greybeards) were trained a physicists.

Wired: "Move Over, Coders—Physicists Will Soon Rule Silicon Valley"

And soon we will rule the world, muhahaha...!

> Move Over, Coders

"Coders" - sure, but not software engineers.

Incidentally, the skill of "coding" (i.e. using a programming language to automate tasks on a computer) should, in my opinion, be taught in school at an early age along with the multiplication table.

iirc dennis ritchie was an applied mathematician.

Confirmed by Netcraft.

Shameless clickbait. TL;DR: Some physicists have become software developers because it pays better than being a physicist. Physicists are smart people overall so they are well suited to solving complex problems that one encounters when developing software.

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