What Are Your Tips for a Mom Getting Her Masters?

00:00:00.000 | [MUSIC PLAYING]

00:00:03.440 | All right.

00:00:06.800 | Let's see here.

00:00:07.520 | We've got time for one more question.

00:00:08.560 | Let me do one more quick, deep work question.

00:00:10.880 | This one comes from Sammy.

00:00:13.080 | Sammy says, what are your tips for a mother of two

00:00:15.960 | small kids doing master's?

00:00:19.280 | Well, I mean, first of all, the fact

00:00:21.140 | that you have two small kids doing master's degrees

00:00:23.080 | tells me that you've got some pretty amazing genes.

00:00:25.240 | So congratulations.

00:00:28.040 | I was trying to explain to my oldest son, who's nine,

00:00:32.680 | the plot of Doogie Howser, M.D., which

00:00:36.480 | was a show that was popular when Jesse and I were kids.

00:00:41.280 | It did not compute.

00:00:43.040 | He was like, wait a second.

00:00:44.680 | There's so many-- and you know what?

00:00:46.440 | It didn't compute not because he couldn't understand it,

00:00:48.480 | but because there are a lot of questions about Doogie Howser,

00:00:51.200 | M.D. And the fact that this 12-year-old was being licensed

00:00:54.720 | to do practice clinical medicine in emergency rooms,

00:00:57.760 | there's a lot of questions about that show.

00:00:59.720 | But that's what I think about when

00:01:01.140 | I think about two small kids doing master's degrees.

00:01:05.360 | There was-- Jesse, there was at--

00:01:07.240 | and I don't mean to go on the side,

00:01:08.740 | but MIT had some of this stuff going on.

00:01:11.320 | Like MIT CS program gets some pretty interesting, weird

00:01:15.480 | brains.

00:01:16.040 | But there was a kid, when I started my PhD program,

00:01:22.120 | he was another incoming computer science student.

00:01:25.480 | And he was 14, maybe, 15, maybe, maybe 16,

00:01:33.320 | but I think like 15 years old.

00:01:35.000 | He had not only finished his undergraduate degree

00:01:37.120 | in computer science from University of Washington,

00:01:39.640 | which is a great program.

00:01:41.440 | He had gone and worked at Microsoft for a while

00:01:44.280 | and was bored.

00:01:45.280 | He was like, I got to go get a PhD.

00:01:46.720 | So he had been in the workforce for a while

00:01:49.560 | before he came back to get his PhD.

00:01:51.840 | And he was like 15 years old.

00:01:53.760 | So it was a strange place.

00:01:55.680 | Did you talk to him a lot?

00:01:57.600 | He was a systems guy, so I didn't know him well.

00:02:00.080 | And I don't think he actually stayed for his PhD.

00:02:03.160 | The problem with PhD programs like at MIT

00:02:05.560 | is the entire time you're there, there's literally people--

00:02:10.880 | not literally, OK, the opposite of literally.

00:02:12.680 | But there's people knocking at your doors

00:02:14.720 | with wheelbarrows full of money.

00:02:17.520 | In reality, it's emails from headhunters, et cetera.

00:02:20.000 | But basically, here is a wheelbarrow full of money.

00:02:23.400 | If you follow me to a job.

00:02:26.600 | And they pick a lot of people off.

00:02:30.240 | You'll just get things from headhunters.

00:02:31.920 | We will-- starting salary, $450,000.

00:02:36.120 | Let's rock and roll.

00:02:36.960 | Come to my quant fund or whatever.

00:02:39.320 | So you lose a lot of people.

00:02:41.240 | They'll get their masters along the way,

00:02:43.480 | and then they're out the door.

00:02:45.280 | So it's only us suckers that actually stick it out

00:02:48.040 | all the way and become low-paid professors.

00:02:51.120 | All right, Sammy, I'm sorry.

00:02:52.240 | I'm completely off your question now.

00:02:53.800 | All right, I was making fun of your ambiguous wording.

00:02:56.840 | Sorry about that.

00:02:57.560 | So let's start this again.

00:02:58.800 | Sammy says, what are your tips for a mother of two

00:03:01.320 | small kids doing masters?

00:03:02.640 | All right, it's a-- what is that, like a dangling modifier?

00:03:05.560 | It's the mother, not the children's doing the masters.

00:03:08.200 | All right, we get that.

00:03:09.760 | How can she find focus time in the midst

00:03:11.640 | of being a wife and a mother?

00:03:16.160 | Let's get ages, nine-year-old and a three-year-old.

00:03:18.440 | I have one of each and a seven-year-old in between.

00:03:21.240 | So I empathize.

00:03:23.000 | Sammy, two things.

00:03:25.520 | One, acknowledge it's a really hard thing

00:03:28.200 | you want to do right now.

00:03:30.360 | So it's important that you don't come into this

00:03:32.360 | with the psychology of, oh, I should just

00:03:35.520 | be able to do this, no big deal.

00:03:38.480 | Let's just rock and roll.

00:03:39.800 | I bought a bullet journal.

00:03:40.920 | We're good.

00:03:42.720 | Let's just go after it.

00:03:44.600 | I read Lean In.

00:03:45.640 | That's really hard.

00:03:47.160 | If you're-- those are hard ages.

00:03:50.520 | The nine-year-old is probably in school,

00:03:52.600 | but the three-year-old might not be.

00:03:54.760 | So that's hard.

00:03:55.560 | Acknowledge that.

00:03:57.160 | Think about it like you told people

00:03:58.640 | I'm going to run a marathon.

00:03:59.840 | We're like, oh, that's so hard.

00:04:01.080 | And that's how you think about it.

00:04:02.120 | Don't think about this as an easy thing to do.

00:04:04.040 | It's not.

00:04:04.720 | So I don't want you to feel bad about this being hard.

00:04:07.640 | Two, in those situations, you need to autopilot all the work.

00:04:11.960 | And by autopilot, this is my terminology.

00:04:15.520 | This goes way back to the early days

00:04:17.360 | of my writing on my website for students.

00:04:20.680 | But autopilot schedules is where all of the work that

00:04:24.480 | needs to be done, you figure out in advance.

00:04:26.280 | This is where it always gets done, when it always gets done.

00:04:29.560 | You can't, in this situation, succeed by just saying,

00:04:33.640 | oh, what's due tomorrow?

00:04:35.600 | Oh, I got to do some readings and write a paper.

00:04:37.960 | Let me go get that work done.

00:04:39.400 | That barely works for 19-year-olds

00:04:41.240 | who are living in a dorm and only doing school.

00:04:43.640 | It's not going to work for a mother of two children.

00:04:45.840 | So you've got to just figure out,

00:04:46.760 | like, this is when my reading gets done.

00:04:48.600 | All right, I dropped a three-year-old off at daycare,

00:04:50.680 | and I have this two-hour window.

00:04:51.840 | And that's always when I do my reading for the English class.

00:04:54.600 | And Sunday afternoons is for paper writing.

00:04:57.880 | So every other Sunday, I work on papers.

00:04:59.560 | You really got to not be thinking at all about,

00:05:01.960 | what should I be doing today?

00:05:03.880 | Autopilot that all out.

00:05:05.000 | Figure out how much time you need,

00:05:06.420 | what work you have to do when it gets done.

00:05:08.920 | So you can be really optimal about this

00:05:10.600 | and really be smart about where you try to fit that time.

00:05:14.320 | If it still doesn't fit, which it might not,

00:05:17.260 | then you have to slow down.

00:05:18.560 | You have to slow down the program.

00:05:19.800 | You have to find a way to do it on a longer timeline.

00:05:21.920 | Face the productivity dragon.

00:05:23.520 | But if the dragon is too big, don't charge into the cave.

00:05:26.480 | That's a great thing about autopilot scheduling

00:05:28.500 | is you get to stare it in the face and say,

00:05:30.560 | can I make this work?

00:05:31.940 | And if you can, this autopilot schedule

00:05:35.520 | is going to give you the best possible chance

00:05:37.600 | of making it work.

00:05:38.440 | And if you can't, you say, okay, what can I make work?

00:05:41.760 | And you adjust what you're doing until it fits.

00:05:45.820 | (upbeat music)

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What Are Your Tips for a Mom Getting Her Masters?

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