6 ms·

What you know matters more than what you do

138 points by ridruejo 14y ago

SatvikBeri 14y ago
I think this point is definitely true for an academic environment, which Cal is in. For the average workplace, the ultimate determinant of your success is what other people believe you have done and can do. This is certainly correlated to what you've done, what you can do, and your ability to promote yourself. In my personal experience, what you choose to do is more important than what you know. I've mentioned a few times that I saved previous employer X $2MM/year, and I did this through finding several problems that seemed like they would be possible to automate, then learning how to automate them-not the other way around.
- roc 14y ago
  In my experience, the "learning how to automate them" implies the analogous step to the "technique"-learning being discussed in the article. e.g. To automate a health care provider's intake and records system well, you really do have to learn a surprising amount of how those people currently do their job. If you understand the job of the intake nurse, the needs of the physicians' assistants, the way records are processed for insurance reasons, the way doctors search and browse, etc., you can save vastly more time and money (to say nothing of improving accuracy and reducing redundancy) than you would with a straightforward records digitization process.
jcfrei 14y ago
terrible title* (not chosen by OP, mind you), that almost put me off - but a very good article. *it should be more along the lines of: what you can learn matters more than what you can do
- nilaykumar 14y ago
  Yep, had the exact same reaction.
iuguy 14y ago
For the workplace I'd say what you do is infinitely more important than what you know, although there is a clear relationship between the two. At the end of the day, people can qualify and measure what you do. Trying to externally measure what someone knows is pretty much impossible to the same extent. Whether you're known as the guy the broke the build, or the guy that automated the build process, you're known by what you do, know what you know, you know?
mgrouchy 14y ago
Interesting that the title of the blog post is "What you know matters more then what you do". I would say this thesis is incomplete. The author talks about how him and his colleagues were trying to figure out the secret sauce of a high achieving researcher. FTA: I hypothesize two things. First, ultra-learning is difficult but it can be cultivated. Second, it might be one of the most important skills for consistently generating impact. Those who are able and willing to continually master hard new knowledge and techniques are playing on a different field than those who are wary of anything that can’t be picked up from a blog post I would possibly agree with his hypothesis at the end of the post, but not the title. I think its likely that this person is a high achiever not just because he is learning quickly and thoroughly, hard topics. He is also applying the requisite work and determination required to be a success. So IMO what you do has to be at least as important as the things you know because that is the only way we have in which to judge your success.
consultutah 14y ago
Just riffing on the title which some below say is a horrible representation of the article, but "who you know, is more important than what you know." As a old timer in the IT industry, I'd simply like to share that the connections that you make with people are much more important than what you know or what you do for your advancement within almost any company. Now you can't usually be totally incompetent, but the stronger your connections are with people above and around you in your organization, the more allowance you ate given for mistakes etc. What am I really trying to say? Get to know people. Don't stay completely head down in your cube knocking out the best code. Get up every once in a while and build relationships with the people around you.
- re_todd 14y ago
  And it's often easier than it sounds. I observed a few popular people and decided to make mental notes of what they talked about, and I was surprised it was mostly trivial nonsense (who won the game, the car chase on the news last night, etc). It doesn't matter, people appreciate that you are just talking to them.
segmond 14y ago
What you know really matters because what you know will determine what you will do. What you do matters but how you do it matters more, and that comes down to knowing different and the best way to do it.
manmal 14y ago
My take on productivity (it is about productivity after all, right?) is that it matters: a) What you choose to do, and b) How well you do it. The second is affected by the domain knowledge you have. Most people don't leave their domain for their whole worklife (they might have secondary domains because of hobbies), and should become experts no matter what (and therefore do well in point b)). Multi-domain genies like Goethe, Da Vinci (, Jobs, Feynman?) have to constantly learn hard to gain enough knowledge in many domains for them to be successful there. Also, I am convinced that such people can do better in a domain they have mastered because of the cross-domain analogies they can apply. Still, what you choose to do (point a)) is as important for productivity. I can be a multi-domain genius scientist and still choose to spend all my time recapitulating achievements of other people, instead of developing a new physics model. Or I can spend years making one app completely bug-free instead of writing 10 new ones. UPDATE: One thing I am really wondering about is: How deep does the knowledge within one domain reach for such personalities? Did Jobs know everything about OS's, or just as much as he needed? My hypothesis is that they all learn just as much as they need to achieve the goal at hand NOW (not hoarding knowledge for the sake of it).
- ArbitraryLimits 14y ago
  > Multi-domain genies like Goethe, Da Vinci (, Jobs, Feynman?) I'm sorry but I just can't let this go without comment. Steve Jobs was not a "multi-domain genius." He had a single, very specific genius, namely gathering lots and lots of ideas and picking the very best one. (IMO the fact that he was an asshole is probably related but not essential.) His entire career was applying this single ability, which he really was a genius of, to multiple domains. He wasn't a genius at anything about those domains per se, just at what he did once he was working in them. I mean, the fact that he had any technical knowledge at all about operating systems or computer graphics already put him in the top tier of "business guys" but that's not the same thing as a deep understanding. For that matter, let's keep going on your list. Richard Feynmann was a smart guy but all his important work (except superfluidity in helium, I guess) was made exploring a single theme, namely how to put quantum mechanics in a Lagrangian formulation rather than a Hamiltonian. He told the story once of the event that sparked his first interest in physics as a career, and it was his high school physics teacher taking him aside and explaining the principle of least action (the basis of the Lagrangian formulation of classical mechanics) - he basically organized the rest of his career around understanding this principle very, very deeply. The reason he's famous in addition to being smart is that he found a new domain to apply it to, namely quantum mechanics. My point is that while mastering a domain of application was important for these people, probably even crucial, it wasn't really the engine of their success. They first developed a single talent or interest, and then looked for a domain to apply it to.
rweba 14y ago
I am also a computer science assistant professor like Cal Newport so I can relate to what he is trying to say here. An academic paper in computer science usually consists of applying a well defined "technique" to a well defined "problem." For example applying "Support Vector Machines" to "Text Classification" or applying "Expert Systems" to "Medical Diagnosis" or applying "Particle Filters" to "Robot localization" The crux of what Newport is referring to is that in practice, many(most?) academic researchers don't deeply learn new techniques that are outside of their immediate research agenda after leaving grad school. They will certainly be AWARE of new techniques and might learn their high level ideas but they won't really learn them deeply enough to be able to improve them or use them in a non-trivial way. It is much much easier to continue exploiting and building upon the techniques they have already mastered than to invest several painful months(years?) to master completely new techniques. So what Newport is suggesting that you should stop applying your current methods that are easy and very productive for you and make a substantial investment of time and energy to master the latest techniques - even if you don't exactly know how you're going to apply them A programming analogy: If you're a C++ programmer, stop doing C++ projects and spend at least 3 months until you're an expert in Python. If you're a Python programmer, stop writing Python and invest several months in becoming an expert in Go. (the analogy is not perfect because mastering a new language is probably a bit easier and more fun than the kind of things Newport is talking about) Here are a couple of thoughts I had on this: (1) How applicable is this idea outside of academic research? For example, in academia there is a big reward for being the FIRST person to apply a given technique to a certain problem, but outside of research being the 2nd or 3rd person to do something can be just fine. (See: Friendster, MySpace, Facebook). So maybe you can afford to wait until someone has shown a great application of a new technique and only then jump in and try to exploit it. (2) An opposing but also convincing idea is that it is better to focus your efforts in one area to avoid spreading yourself too thin. Such focus allows you to gain "comparative advantage" and to easily do things that are difficult for people who don't have your deep experience. In other words, it is better to spend your efforts trying to become the world's greatest Python hacker than to jump on the bandwagon of every new programming language that comes out and ending up as a "Jack of all trades, master of none." My conclusion: I do think it is worthwhile to challenge yourself not to just stick to what you already know (which in my personal experience is VERY easy to do especially if you find that you're very productive using what you know). But you also have to be selective. Life is too short to try to be a master of everything. And there is great value in gaining a very deep expertise in a particular topic or technique.
gwern 14y ago
> According to my colleagues, this star researcher tends to begin with techniques, not problems. He first masters a technique that seems promising (and when I say “master,” I mean it — he really goes deep in building his understanding). He then uses this new technique to seek out problems that were once hard but now yield easily. He’s restless in this quest, often mastering several new techniques each year. The Feynman algorithm for looking like a genius!
billswift 14y ago
The commenters here seem to have missed what I thought was Cal's point: What you know matters more than what you do, because what you can do is limited by what you know. Learning new techniques is important because it expands the arena of what you can do.
- manmal 14y ago
  That's really a chicken-egg problem, no? We learn by doing (as children), and knowledge is better stored in our brains if it's supplemented with "doing".
- randomdata 14y ago
  I think that is a given: Once you've done something, you intrinsically know how to do it. More interestingly, to what degree does our knowledge play on our ability to see new ideas? Do our brains ignore the seemingly impossible because it seems impossible? Could someone have imagined Facebook before computers and the internet were invented, for example? Do the ideas come first, and then one set out to figure out how to make it work, like as it appears to happen in science fiction? Or are those ideas already based on working knowledge of what is reasonably possible with not-so-far-off technology?
- damoncali 14y ago
  The entire point seems to be "you must know how to do something before you do it". Academics seem to revel in stating the obvious in a formal tone with big words.
bicknergseng 14y ago
I would tweak his hypothesis: 1. It is now easier than ever to achieve "ultra-learning" because the vast majority of knowledge is available everywhere for free online. The "learn to code the hard way" crowd will disagree, but I would say working knowledge of almost anything can be gained in between a week and a month, depending on how specific the topic (eg creating websites: week, creating a kernel: month). You will NOT be an expert at either, but you will be someone who can imitate. Imitation and application of existing knowledge is extremely quick once you learn how to learn how to learn. I would say the researcher from the article and Steve Jobs are masters of the "working knowledge and apply" school, but the low barrier to entry explains why every day we all read about the "LinkedIn for this" or "Instagram for that." 2. I would define "deep-knowledge" as knowledge that doesn't already exist or isn't already accessible. This is true innovation or discovery. This is the internet, nuclear fission, DNA sequencing. This kind of knowledge is rare because it takes time, persistence, and capability. One has to gain working knowledge and keep digging until you hit the bottom of all knowledge about the topic... and then break out a drill bit and push on, or go to the tallest building in the world and add a story on top. Unfortunately, our society rewards the first category far more than the second.
- loxs 14y ago
  See my other comments in the thread to find out about me. 1. This is true. Weeks to month(s) is usually enough to learn enough to roughly be aware of the kinds of problems you can solve with a tool. Finding the right and interesting problem is not quite as easy though. Usually after I know enough to have a feeling about the kinds of problems, I stop learning the new tool. It's not interesting to learn more just for the sake of learning. Once I find a good problem(s), I put months or years in learning the tool and solving problems with it. One of the reasons I moved away from medicine is because you can't learn while solving problems. You have to learn a lot upfront before you are allowed to the "field". This is really justified, but I lost interest. 2. This is not really always the case. There are fields (medicine for example), where true innovation is not always result of "deep knowledge". For example antibiotics, vaccines etc. In those cases true innovation happened because of trying to solve a problem (and quite accidentally), not because someone by his really deep knowledge predicted these phenomena. I wouldn't try to judge which way is "better". And I wouldn't use the word "unfortunately" for these. Probably we need very much both to progress.
deleted 14y ago
[deleted]

138 points by ridruejo 14y ago

I think this point is definitely true for an academic environment, which Cal is in. For the average workplace, the ultimate determinant of your success is what other people believe you have done and can do. This is certainly correlated to what you've done, what you can do, and your ability to promote yourself. In my personal experience, what you choose to do is more important than what you know. I've mentioned a few times that I saved previous employer X $2MM/year, and I did this through finding several problems that seemed like they would be possible to automate, then learning how to automate them-not the other way around.

roc 14y ago
In my experience, the "learning how to automate them" implies the analogous step to the "technique"-learning being discussed in the article. e.g. To automate a health care provider's intake and records system well, you really do have to learn a surprising amount of how those people currently do their job. If you understand the job of the intake nurse, the needs of the physicians' assistants, the way records are processed for insurance reasons, the way doctors search and browse, etc., you can save vastly more time and money (to say nothing of improving accuracy and reducing redundancy) than you would with a straightforward records digitization process.

jcfrei 14y ago

terrible title* (not chosen by OP, mind you), that almost put me off - but a very good article. *it should be more along the lines of: what you can learn matters more than what you can do

nilaykumar 14y ago
Yep, had the exact same reaction.

iuguy 14y ago

For the workplace I'd say what you do is infinitely more important than what you know, although there is a clear relationship between the two. At the end of the day, people can qualify and measure what you do. Trying to externally measure what someone knows is pretty much impossible to the same extent. Whether you're known as the guy the broke the build, or the guy that automated the build process, you're known by what you do, know what you know, you know?

mgrouchy 14y ago

Interesting that the title of the blog post is "What you know matters more then what you do". I would say this thesis is incomplete. The author talks about how him and his colleagues were trying to figure out the secret sauce of a high achieving researcher. FTA: I hypothesize two things. First, ultra-learning is difficult but it can be cultivated. Second, it might be one of the most important skills for consistently generating impact. Those who are able and willing to continually master hard new knowledge and techniques are playing on a different field than those who are wary of anything that can’t be picked up from a blog post I would possibly agree with his hypothesis at the end of the post, but not the title. I think its likely that this person is a high achiever not just because he is learning quickly and thoroughly, hard topics. He is also applying the requisite work and determination required to be a success. So IMO what you do has to be at least as important as the things you know because that is the only way we have in which to judge your success.

consultutah 14y ago

Just riffing on the title which some below say is a horrible representation of the article, but "who you know, is more important than what you know." As a old timer in the IT industry, I'd simply like to share that the connections that you make with people are much more important than what you know or what you do for your advancement within almost any company. Now you can't usually be totally incompetent, but the stronger your connections are with people above and around you in your organization, the more allowance you ate given for mistakes etc. What am I really trying to say? Get to know people. Don't stay completely head down in your cube knocking out the best code. Get up every once in a while and build relationships with the people around you.

re_todd 14y ago
And it's often easier than it sounds. I observed a few popular people and decided to make mental notes of what they talked about, and I was surprised it was mostly trivial nonsense (who won the game, the car chase on the news last night, etc). It doesn't matter, people appreciate that you are just talking to them.

segmond 14y ago

What you know really matters because what you know will determine what you will do. What you do matters but how you do it matters more, and that comes down to knowing different and the best way to do it.

manmal 14y ago

My take on productivity (it is about productivity after all, right?) is that it matters: a) What you choose to do, and b) How well you do it. The second is affected by the domain knowledge you have. Most people don't leave their domain for their whole worklife (they might have secondary domains because of hobbies), and should become experts no matter what (and therefore do well in point b)). Multi-domain genies like Goethe, Da Vinci (, Jobs, Feynman?) have to constantly learn hard to gain enough knowledge in many domains for them to be successful there. Also, I am convinced that such people can do better in a domain they have mastered because of the cross-domain analogies they can apply. Still, what you choose to do (point a)) is as important for productivity. I can be a multi-domain genius scientist and still choose to spend all my time recapitulating achievements of other people, instead of developing a new physics model. Or I can spend years making one app completely bug-free instead of writing 10 new ones. UPDATE: One thing I am really wondering about is: How deep does the knowledge within one domain reach for such personalities? Did Jobs know everything about OS's, or just as much as he needed? My hypothesis is that they all learn just as much as they need to achieve the goal at hand NOW (not hoarding knowledge for the sake of it).

ArbitraryLimits 14y ago
> Multi-domain genies like Goethe, Da Vinci (, Jobs, Feynman?) I'm sorry but I just can't let this go without comment. Steve Jobs was not a "multi-domain genius." He had a single, very specific genius, namely gathering lots and lots of ideas and picking the very best one. (IMO the fact that he was an asshole is probably related but not essential.) His entire career was applying this single ability, which he really was a genius of, to multiple domains. He wasn't a genius at anything about those domains per se, just at what he did once he was working in them. I mean, the fact that he had any technical knowledge at all about operating systems or computer graphics already put him in the top tier of "business guys" but that's not the same thing as a deep understanding. For that matter, let's keep going on your list. Richard Feynmann was a smart guy but all his important work (except superfluidity in helium, I guess) was made exploring a single theme, namely how to put quantum mechanics in a Lagrangian formulation rather than a Hamiltonian. He told the story once of the event that sparked his first interest in physics as a career, and it was his high school physics teacher taking him aside and explaining the principle of least action (the basis of the Lagrangian formulation of classical mechanics) - he basically organized the rest of his career around understanding this principle very, very deeply. The reason he's famous in addition to being smart is that he found a new domain to apply it to, namely quantum mechanics. My point is that while mastering a domain of application was important for these people, probably even crucial, it wasn't really the engine of their success. They first developed a single talent or interest, and then looked for a domain to apply it to.

rweba 14y ago

I am also a computer science assistant professor like Cal Newport so I can relate to what he is trying to say here. An academic paper in computer science usually consists of applying a well defined "technique" to a well defined "problem." For example applying "Support Vector Machines" to "Text Classification" or applying "Expert Systems" to "Medical Diagnosis" or applying "Particle Filters" to "Robot localization" The crux of what Newport is referring to is that in practice, many(most?) academic researchers don't deeply learn new techniques that are outside of their immediate research agenda after leaving grad school. They will certainly be AWARE of new techniques and might learn their high level ideas but they won't really learn them deeply enough to be able to improve them or use them in a non-trivial way. It is much much easier to continue exploiting and building upon the techniques they have already mastered than to invest several painful months(years?) to master completely new techniques. So what Newport is suggesting that you should stop applying your current methods that are easy and very productive for you and make a substantial investment of time and energy to master the latest techniques - even if you don't exactly know how you're going to apply them A programming analogy: If you're a C++ programmer, stop doing C++ projects and spend at least 3 months until you're an expert in Python. If you're a Python programmer, stop writing Python and invest several months in becoming an expert in Go. (the analogy is not perfect because mastering a new language is probably a bit easier and more fun than the kind of things Newport is talking about) Here are a couple of thoughts I had on this: (1) How applicable is this idea outside of academic research? For example, in academia there is a big reward for being the FIRST person to apply a given technique to a certain problem, but outside of research being the 2nd or 3rd person to do something can be just fine. (See: Friendster, MySpace, Facebook). So maybe you can afford to wait until someone has shown a great application of a new technique and only then jump in and try to exploit it. (2) An opposing but also convincing idea is that it is better to focus your efforts in one area to avoid spreading yourself too thin. Such focus allows you to gain "comparative advantage" and to easily do things that are difficult for people who don't have your deep experience. In other words, it is better to spend your efforts trying to become the world's greatest Python hacker than to jump on the bandwagon of every new programming language that comes out and ending up as a "Jack of all trades, master of none." My conclusion: I do think it is worthwhile to challenge yourself not to just stick to what you already know (which in my personal experience is VERY easy to do especially if you find that you're very productive using what you know). But you also have to be selective. Life is too short to try to be a master of everything. And there is great value in gaining a very deep expertise in a particular topic or technique.

gwern 14y ago

> According to my colleagues, this star researcher tends to begin with techniques, not problems. He first masters a technique that seems promising (and when I say “master,” I mean it — he really goes deep in building his understanding). He then uses this new technique to seek out problems that were once hard but now yield easily. He’s restless in this quest, often mastering several new techniques each year. The Feynman algorithm for looking like a genius!

billswift 14y ago

The commenters here seem to have missed what I thought was Cal's point: What you know matters more than what you do, because what you can do is limited by what you know. Learning new techniques is important because it expands the arena of what you can do.

manmal 14y ago
That's really a chicken-egg problem, no? We learn by doing (as children), and knowledge is better stored in our brains if it's supplemented with "doing".
randomdata 14y ago
I think that is a given: Once you've done something, you intrinsically know how to do it. More interestingly, to what degree does our knowledge play on our ability to see new ideas? Do our brains ignore the seemingly impossible because it seems impossible? Could someone have imagined Facebook before computers and the internet were invented, for example? Do the ideas come first, and then one set out to figure out how to make it work, like as it appears to happen in science fiction? Or are those ideas already based on working knowledge of what is reasonably possible with not-so-far-off technology?
damoncali 14y ago
The entire point seems to be "you must know how to do something before you do it". Academics seem to revel in stating the obvious in a formal tone with big words.

bicknergseng 14y ago

I would tweak his hypothesis: 1. It is now easier than ever to achieve "ultra-learning" because the vast majority of knowledge is available everywhere for free online. The "learn to code the hard way" crowd will disagree, but I would say working knowledge of almost anything can be gained in between a week and a month, depending on how specific the topic (eg creating websites: week, creating a kernel: month). You will NOT be an expert at either, but you will be someone who can imitate. Imitation and application of existing knowledge is extremely quick once you learn how to learn how to learn. I would say the researcher from the article and Steve Jobs are masters of the "working knowledge and apply" school, but the low barrier to entry explains why every day we all read about the "LinkedIn for this" or "Instagram for that." 2. I would define "deep-knowledge" as knowledge that doesn't already exist or isn't already accessible. This is true innovation or discovery. This is the internet, nuclear fission, DNA sequencing. This kind of knowledge is rare because it takes time, persistence, and capability. One has to gain working knowledge and keep digging until you hit the bottom of all knowledge about the topic... and then break out a drill bit and push on, or go to the tallest building in the world and add a story on top. Unfortunately, our society rewards the first category far more than the second.

loxs 14y ago
See my other comments in the thread to find out about me. 1. This is true. Weeks to month(s) is usually enough to learn enough to roughly be aware of the kinds of problems you can solve with a tool. Finding the right and interesting problem is not quite as easy though. Usually after I know enough to have a feeling about the kinds of problems, I stop learning the new tool. It's not interesting to learn more just for the sake of learning. Once I find a good problem(s), I put months or years in learning the tool and solving problems with it. One of the reasons I moved away from medicine is because you can't learn while solving problems. You have to learn a lot upfront before you are allowed to the "field". This is really justified, but I lost interest. 2. This is not really always the case. There are fields (medicine for example), where true innovation is not always result of "deep knowledge". For example antibiotics, vaccines etc. In those cases true innovation happened because of trying to solve a problem (and quite accidentally), not because someone by his really deep knowledge predicted these phenomena. I wouldn't try to judge which way is "better". And I wouldn't use the word "unfortunately" for these. Probably we need very much both to progress.

deleted 14y ago

[deleted]