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  • >> Hi, y'all. Before I start just very quick announcement. If you bought anything at the

  • wonderful PyLadies auction last night you need to pick it up at registration. During

  • the break. I've already picked my tie up. >> Nice tie!

  • >> Thank you. Good morning I'm Jacob Kaplan-Moss I'm one of the contributors to Django and

  • I'm the director of security at Heroku. When I say thank you I really mean it. It's hard

  • for me to over state just how fantastic it is to be up here speaking to you. This is

  • the highlight of my career. This is the apex of everything that I've done

  • and I'm not exaggerating this community means more to me than anything in tech, in my professional

  • life. I've spoiled the punch line there, didn't I?

  • I've been coming here to PyCon for 11 years. This is my 11th PyCon this is the 10th anniversary

  • of first getting on stage. I gave me first talk at PyCon in 2005 in Washington, D.C.

  • I gave a talk on app script, it was utility to bridge apple's AppleScripting framework

  • with Py on this. Also in 2005 Adrian gave a tech demo of some tools that we were building

  • internally at the newspaper in Lawrence, Kansas, using to build our websites. Those tools became

  • Django and now Django is ten years old and is used all around the world by thousands

  • of companies in many different industries. I've watched this conference go from 300 people

  • to close to 3,000. I've watched our community explode with new faces and growing diversity.

  • I've been involved in planning this conference, in membership with the PSF in discussions

  • about core language development. I've been floating in and out of this community at various

  • levels for a decade. When I say this is apex of my professional career I really mean it.

  • This is pretty incredible. The problem is that right now my main emotion is in security.

  • As a voice in the back of my head that's telling me, you don't deserve to be here. Look at

  • the amazing people you shared this stage with. Who are you?

  • What did you do to get up on stage with them? You don't belong on stage with them. You don't

  • belong or deserve this success. Jay smooth calls this voice the little hater.

  • Oh, man, audio. >> Let's try that again. ���

  • >> Doctrine is back in effect. I haven't done a video in a long time. Been a few reasons

  • for that. Reason number one, both my cameras managed to stop working. Reason number two,

  • I was busy with my first ever trip to Los Angeles. For this big web video event called

  • "the winnis"e shout out to everybody. The other reason I haven't been doing videos for

  • awhile basically, I don't always feel like I'm cut out to do this stuff. I'm sure some

  • people wake up every day feeling confident that the entire world wants to look at their

  • face and listen to them talk. But I'm not one of those people. When I'm in the groove

  • of getting work done I feel like I'm making a connection with you guys out there and my

  • ideas are resonating with you, it feels natural to keep showing up and maintaining that connection.

  • But if I go too long without putting work in it feels like that connection is broken,

  • there's a little voice inside my head that starts playing tricks on me trying to convince

  • me that the connection was never really there. I think this is true for all creative people

  • that we each have a little hater that lives inside our heads tries to set up traps for

  • us. Jay's awesome. Everything that he's done is

  • fantastic. When I hear him talking about his insecurity I think about my little hater,

  • my little hater tells me that I don't deserve my success. The really insidious thing about

  • this voice for me is that there's like a little bit of him that's right. And this isn't like

  • a humble brag I'm not being false modest right now, there are things that I've done that

  • I'm incredibly proud of there are things that I've been involved in and things that I've

  • led that I feel do qualify me to be up here. But I don't think they're the things that

  • you're thinking about. You probably think I'm here because I'm the inventor of Django.

  • Right? But that's not actually true. If you know

  • a little more about the story about how Django came to be you might know the co-creator of

  • Django. That's actually not really true either. The fact is that I was hired to work at the

  • newspaper a year after Adrian and Simon invented and co-created Django.

  • Really I'm the guy who got hired to work on the thing a year after it was already made.

  • But people still assume that I'm somehow responsible, people assume that this is because I'm incredibly

  • programmer that I'm a rock star, a Ninja or whatever it is that recruiters are calling

  • it these days. And people suspect that I'm successful because of my programming skill.

  • That's not true. I'm at best an average programmer. This is Anne Trason probably the most accomplished

  • ultra marathoner in the history of the sport. What's ultra running?

  • Ultra running is any distance over a marathon over 26 miles. Most of these races are on

  • trails, most of them involve substantial climbing, technical terrain, crossing streams and rivers,

  • some of the hardest races can take multiple days of continuous running to complete. The

  • common distances in the sport are 50k, 50 miles, and a hundred miles. For those who

  • haven't heard of ultra running, a quick FAQ, people really run that far?

  • Yes. Really? Yes. This is the map of the western states

  • 100 it runs from squaw valley in California, all the way down to Auburn, again a hundred

  • miles over a pass, all on trails. Ann won western states 14 times over about two decades.

  • She said, dozens of course records across the country in the '80s and '90s many which

  • still stand today 10 or 20 years later. In 1994, she set the course record at the leadville

  • 100, this is 100 mile race mostly above 10,000 feet. Breathing up there is like breathing

  • through a straw. Her course record, very few people have come within an hour of her record.

  • It's never even close. That record stood for 20 years and no one's come within smelling

  • distance of it. It's hard to think about -- hard to over estimate just how dominant, just how

  • exceptional Ann was in her sport. I'm talking about this because as of few weeks ago I get

  • to call myself an ultra runner as well. I finished my first 50k.

  • [ Applause ] Thank you. I'm of course not even remotely

  • in the same league as someone like Ann Trason but I am, I do get to wear the same label.

  • I'm a mediocre runner. I finished 535th out of about a thousand. Felt pretty good. The

  • thing that is interesting about this sport is that we can quantify it. We have lot of

  • numbers that help us figure out how to measure performance, we can talk about pace, talk

  • about distance, we can talk about elevation change, the technicality of the terrain. There's

  • a lot of ways that we can account for the skill gap between me and elite athlete like

  • Ann. There's one site ultrasignup.com main Url to sign up creative. They calculate they

  • call runner's score. Mine is 68%, that means across all of the races that I've run I typically

  • finish in about 68% slower than the winner. So the winner score is go to be 100% someone

  • just barely doesn't win is going to be 99% I'm down there like 68%. Over states it a

  • little bit, what is cool about this you can look at it broken down by distance so I can

  • see that my performance falls off as I run farther which means I got to work on my endurance.

  • My score is 68% Ann's is 98.58%. In other words, she usually wins. Right?

  • This isn't surprising that there is that much of a gap between me and average runner elite

  • runner. If we break down -- were to plot a histogram of all the various times and scored

  • we'd come up with a shape like this. This should be familiar shape to anyone who has

  • done any rudimentary statistics, a bell curve, a normal distribution. Most people are average

  • and few people are exceptionally good, few people are exceptionally bad on the skinny

  • ends of the curve. This isn't a surprise if we see -- when we see this pattern we call

  • it a normal curve. Because we see it everywhere. Almost every skill that we know how to measure

  • ends up showing a distribution something like this. With most people behaving around in

  • the middle and few people exceptionally good and few people exceptionally bad. When I said

  • I was mediocre programmer some of you probably didn't believe me. I sort much set it up,

  • I led up to it, I spoiled the surprise you probably didn't believe me when I said that.

  • But why? Right?

  • We know that most skill tends to fall into this normal distribution. So, most of you,

  • the vast majority of you never actually worked with me, why would you assume that my coding

  • ability is exceptional? Why do you think I'm out there?

  • What is it about what you've heard, what you see, what you think you know, that makes you

  • Intuit that I'm under the skinny end? The absence of any other data shouldn't you

  • assume that I'm solidly in the middle? Well part of this is that, how do we even

  • measure coding ability? We're -- we are so -- we are infants in figuring

  • how to measure our ability to produce software. What are our metrics?

  • Lines of code? What does that measure?

  • Story points? What even is a story point?

  • We'd like to think we work in a field that's very logical and analytical and fact-based.

  • But the truth is we don't have any idea how to talk about what we do in any sort of technical

  • or systematic way. We do what humans do when we don't have data we make up stories. This

  • is a human thing, we do it. We tell ourselves anecdotes and stories about people's ability

  • to program. The problem is that these stories are simplistic, this is what we do as human

  • beings. We stereotype. We architype we say that people suck at programming.

  • We say that people rock at programming. We don't leave a lot of room for the in betweens.

  • We bucket everyone we know into an amazing programmer or a worthless use of a seat. We

  • seem to fall into this assumption that skill in our industry is distributed in a U-shaped

  • curve. Most people are either on one end of the extreme which doesn't make a lot of sense.

  • Aren't you learning throughout our career? How would we go from being absolutely terrible

  • to absolutely wonderful any sort of in between. But we only tell ourselves these two stories

  • allow ourselves two possible narratives, why did you think that I was under the skinny

  • end? Well, you have two narratives, you have crappy

  • crammers and amazing programmers you know that I'm associated with Django so the crappy

  • one doesn't seem to fit you pick the good one. Makes sense, right?

  • But we know again that if we could measure programming ability would look like this.

  • Why? Because most people are average at most things.

  • This is not lake Wobegon most people are not above average. The assumption that programming

  • falls into this bi-modal distribution is dangerous and a myth. And this talent myth sets up a

  • world you can't program unless you're a rock star or a Ninja. And it's actively harmful

  • this myth is keeping people from programming. It's driving them out of programming and it's

  • preventing most of the growth and improvement that we'd like to see.

  • [Applause] The U.S. bureau of labor statistics estimates

  • that by 2020 there's going to be a 1.5 million job gap. In other words, there is going to

  • be a million and half unfilled programming jobs in the U.S.

  • By 2020. Five years. The EU published similar numbers, 1.2 million in 2018, three years.

  • It's clear from sort of industry standpoint take really broad view we have to be doing

  • something to get more people into our industry. Right?

  • Sometimes talk about this as the pipeline problem, we need to get more people into tech.

  • The talent myth is a filter on the front of that pipeline. Prevents the m from getting

  • into this career, into this job. Into learning the skill. If we believe talent is Bimodal.

  • It lends to the belief that either born with programming ability or not. That it's innate

  • attribute. That you have an innate talent or you simply don't. Somebody who doesn't

  • know how to write software who believes this myth, I'm 30 or 20 or 15 and I haven't written

  • code yet I'm never going to. Or 40 or 60. Only options are amazing or terrible we believe

  • that you have to be passionate about your career. You have to think about programming

  • every waking moment of your life because if there's only two options the minute you take

  • your eye off of it, you're going to slide right from amazing to terrible again. So we

  • have to always be working in our off hours, have to be working crazy hours at work have

  • to study on our own time, et cetera. Last year over half a million people ran a marathon.

  • Did all of them have innate talent? I doubt it. I didn't. I didn't start running

  • until I was 25. Most of them like me ran this marathon rather badly. A few people out front

  • ran it very, very fast and all of these people, pretty slowly. We don't believe you have to

  • be passionate about running. All of it takes is a pair of shoes. Don't even believe you

  • have to particularly like it. How many runners do you know that kind of hate it?

  • Someone asked me one time what the best part about running the 50k was I said it was finishing.

  • Running a pair on this is really hard. It's 26 miles, takes sometimes years of training,

  • thousands of miles, it requires commitment and focus. Do you think that writing software,

  • that writing Python is harder than running a marathon?

  • Why aren't there half a million people here today?

  • Why do we tell ourselves such different stories about one skill, coding and another skill,

  • running. I want to tell you a little story of what these narratives do to individuals.

  • Couple years ago I was at Kansas university's GIS day, GIS day are these one-day GIS, geographic

  • information systems events held at universities all around the world. I love going to K.U.

  • it's always exciting and interesting, I'm a total dabbler in GIS but great seeing professionals

  • talk about it. Each year in the afternoon students give presentations about their projects.

  • These are technically detailed, really interesting talks about original research, any one of

  • those talks could be a session here at PyCon you'd all think it was the best on the schedule.

  • Why aren't these speakers here? Well, one student a few years ago gave a particularly

  • fantastic presentation, it was about predicting seasonal floods on the Kansas river using

  • a combination of historical data and flow analysis and rainfall data those sorts of

  • things. The tools that used should be pretty familiar to a lot of people in the room. She

  • used Amazon web services, Linux, Postgres QL, Django, geodjango. I was hiring Python

  • developers, she had just written thousands of lines of Python code. I asked her if she

  • wanted in to interview with us. No, no, I can't do that. I'm not really a programmer.

  • This is woman who had just invented her own cluster distribute geographic information

  • systems processing data pipeline. But she's not really a programmer. Because programming

  • is something you in myth not something you do. The truth is that programming isn't passion,

  • it isn't a talent just like some things that you can learn. It's not even one thing, I've

  • been talking about programming as if it were a single thing, but that's not actually true.

  • Alex when he was reading a draft pointed out to me software development requires all sorts

  • of skills, coding is just like one small part of it. We need design, we need communication,

  • we need writing. We need debugging we need to have at least one person who understands

  • Unicode. [ laughter ]

  • There are multiple independent skills, when we assume that they're all required to be

  • a programmer, we assume that a person is the minimum of their parts. Sure, you might be

  • able to design well and speak well and write well and you're a great project manager but

  • you don't know how linked list works so get out of the building. Like any skill you can

  • program, you can do any of these skills professionally or occasionally you can do them as a hobby

  • or part-time job, as a full-time job. You can do them badly. You can do them well or

  • most likely you can do them average. If we embrace this idea that it's cool to be okay

  • at these skills, that being average is fine, it will be less intimidating for newcomers.

  • If we set the bar for success at okay rather than at exceptional, that bar seems a lot

  • easier to clear for people new to our community. Even once we get people in, the talent myth

  • haunts people who are already in our community. And it actively drives people out of tech.

  • This is the part of a talk that's a little bit of a downer. But we have to acknowledge

  • it, it's rife with sexism, racism, homophobia. There isn't one cause, but the talent isn't

  • one of them. It contributes to these. In our industry we have the myth of the brilliant

  • Asshole. The 10X programmer who is so good at his job that people have to work with him.

  • He has such great output that, yeah, he's a jerk, but we still have to work with him

  • anyway. The argument goes that we need this output so the toxic behavior is acceptable.

  • Now in reality we know that skill is normally distributed, so it's likely that these people

  • aren't actually exceptional. But even if we grant that they are, how many developers does

  • a 10X programmer have to drive away before they -- before it's awash?

  • [Applause] More insidiously this idea that programmers

  • fall into either sucks or rocks categories gives us dangerous Bias I want you to think,

  • answer this question. What does a 10X programmer look like?

  • We probably thought of someone like this, mark Zuckerberg, a young white man that's

  • who we have seen on the media, don't spoil it. That's who we've seen on the media. In

  • tech company mastheads and so on. But that's not actually mark, that's Jessie Isenberg

  • who played him in a major motion picture about a young white man founding a tech company.

  • That's mark Zuckerberg. Sorry that's not. That's Andy sandberg, Andy is an actor on

  • "Saturday night live" who played Jessie Isenberg playing mark Zuckerberg, I think that's mark

  • Zuckerberg. I'm trying to be funny here, I guess for some of you I succeeded.

  • There's a point here, this young white man architype is so common that we need three

  • people to play the same person. Which one is -- anyway. When we see someone who doesn't

  • fit that, doesn't look like these three men, we assume that they're not a real programmer

  • because that is what a real programmer looks like. Almost all the women in this industry

  • I know have a story about somebody assuming that they're not actually a programmer. I've

  • talked with multiple women here at PyCon this year, who have </