CEFR_C2 (IELTS 8-8.5)

2. How to turn around a city (subtitles)

2022-01-09 18:10:03 simyang 0


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00:13

Has anyone here been to Fresno?

00:16

OK, good, good.

00:17

That’s where I’m from,

00:18

where I was born

00:19

and where I live today.

00:22

For those of you less familiar,

00:24

Fresno and the entire Central Valley of California

00:27

is a place that’s built by agriculture:

00:30

miles and miles of farmland for as far as the eye can see

00:33

with a couple of large, poor cities dotting the landscape.

00:38

My family, like much of the local population,

00:40

is a family of immigrant farm laborers:

00:43

those toiling away in the fields hoping for a ¢25-an-hour raise.

00:48

I didn’t see myself destined for the glamour of Silicon Valley,

00:53

but I did find my way to college, and something miraculous happened.

00:58

I got a job in tech.

01:00

And I remember the first time I didn’t have to count the change

01:03

when trying to figure out how much to tip for pizza delivery,

01:07

when I realized that this industry,

01:09

the technology industry,

01:10

was going to change my life forever.

01:13

And I remember thinking to myself,

01:16

if it can happen to me,

01:18

a poor, queer Brown woman from nowhere,

01:22

why can’t it happen to entire cities of people like me?

01:26

And so for the last eight years,

01:27

that’s what I’ve been working on in Fresno:

01:30

building a business that could expose what it takes to cause an entire city --

01:35

and not just a select few people in it --

01:37

to thrive.

01:39

It turns out we only need three pretty simple ingredients.

01:43

Training, proof and community.

01:49

So the cornerstone of everything that we do is job training.

01:53

The communities that we work with are often from very poor populations,

01:57

maybe folks who are learning English as a second language,

02:01

maybe they were unhoused,

02:02

the formerly incarcerated,

02:03

veterans,

02:04

folks who are very often from retail or factory work.

02:09

These folks, their issue is not their ability to learn technical things.

02:14

Their problems center on things that are a lot less obvious.

02:18

Things like childcare,

02:20

transportation,

02:22

hunger,

02:24

money.

02:25

So those are the things that we focus on.

02:28

It can be especially hard on families.

02:31

How do you justify learning to do something like write code

02:35

when there are bills to pay?

02:37

Wouldn’t it be better for the family if you just got a job at McDonald’s

02:40

and put in as many hours as you can?

02:42

Because that’s a check,

02:45

and who’s going to watch your little brother?

02:48

That’s what we do as a family;

02:49

we pitch in.

02:51

But how do you justify to the people around you

02:54

when it looks to them like you’re just playing around on the computer?

02:58

We didn't invent a new way to teach JavaScript.

03:03

We just focus a lot more

03:04

on the things that actually prevent people from learning it.

03:08

In addition to connecting our students to things like bus tokens

03:11

and free regional transit options,

03:14

we also just deploy a fleet of vehicles

03:16

whose only job is to pick these folks up before their study groups

03:20

and drop them back off after class.

03:23

If they need food, we get them food.

03:25

We work with food cupboards and pantries,

03:29

making sure that boxes of food are delivered to these students’ homes

03:34

with enough for a family of three to five people.

03:37

We connect them to childcare options

03:39

that make sense for their schedules and their budgets.

03:41

But most importantly,

03:43

because cash is such a center of energy and decision-making

03:47

for these families,

03:49

through our apprenticeship program,

03:52

we literally pay them to learn.

03:55

So not only do they get to earn a wage

03:58

and are exposed to real-world work,

04:01

but now they also have that first line on the resume.

04:04

The one that’s so hard to get

04:06

and the one that builds confidence in the rest of the world

04:09

that you might know what you’re talking about.

04:12

And so you might be thinking to yourself,

04:15

“OK, Irma, this sounds great,

04:18

but it sounds really expensive.”

04:21

So how do you pay for it?

04:23

We’ve turned a long-held idea on its head.

04:27

We have to stop putting the burden --

04:29

the financial burden --

04:30

on the student and the families who are already struggling

04:33

and start putting it on the people

04:35

and the entities that benefit most from their untapped potential.

04:40

Entities like government,

04:42

corporations,

04:43

philanthropy.

04:45

These are the entities that benefit from the development of that talent,

04:48

and so that's who we get to pay for it.

04:50

Let’s throw back the curtain on what I’m trying to say here.

04:56

Let's take the government.

04:57

The US spends a trillion dollars scaling up a workforce for this country.

05:04

Many of those programs have mixed results,

05:06

and while some folks who come out of them do in fact earn higher wages at the end,

05:10

while they’re still learning,

05:12

when they’re still in training,

05:13

many of these folks can’t also work,

05:16

which means that they’re not bringing home a check,

05:18

which means that they’re still in survival mode,

05:20

which means that the people who would benefit most can’t participate

05:24

to begin with.

05:26

That’s where a system like ours makes some sense.

05:29

We apply for allocations of that same kind of money,

05:32

and use it to pay people to learn.

05:35

We also work with corporations.

05:38

QA testing, for example, is a job that can be taught

05:42

and a role that companies desperately need.

05:46

Training up a batch of QA engineers is low-hanging fruit

05:50

and has almost instant results for companies.

05:54

For the companies to invest in the development of that talent,

05:58

it breeds them a local and eager technology workforce from which to choose.

06:04

Companies that are in a growth mode

06:06

or who are experiencing a digital transformation,

06:08

they know that the key to their future

06:11

is their ability to find, hire and retain talent.

06:14

We can train up entire cohorts

06:16

or a generation of junior-level and apprentice-level technologists

06:20

trained directly to their systems,

06:22

ready to be hired on day one.

06:25

We’ve worked with all kinds of companies,

06:28

getting them to pay for things like tuition

06:30

and money for students to accomplish exactly this goal.

06:34

Philanthropy’s interests here may be even easier to describe.

06:38

Foundations and nonprofits,

06:41

they want to see their money put to good use.

06:43

Take the Quality Jobs Fund, for example.

06:46

It’s a collaborative effort

06:47

between the Federal Home Loan Bank of San Francisco

06:50

and the New World Foundation,

06:52

and their express mission is to address inequality

06:57

through quality jobs expansion and skills development.

07:01

We apply for allocations or grants from philanthropies like those,

07:06

work with the government dollars that we just described,

07:09

and companies in the way that we just talked about,

07:11

put it all together to use it to pay people to learn.

07:16

So that’s how you pay for it.

07:19

Now, what is it that these folks should learn on?

07:23

In our view, it’s real-world software projects,

07:26

because that is the proof.

07:29

You see,

07:31

all of the software that the world needs built has to get made.

07:36

And so we can leverage talent from these underrepresented communities

07:40

to deliver on that need,

07:42

build a training ground for green talent

07:46

and also build a really robust business.

07:50

We’ll take OnwardUS as just one example.

07:53

It was a rapid-response initiative in response to COVID

07:58

where we partnered with the Kapor Center.

08:00

It was adopted by the state of California and then 10 other states.

08:04

The idea was to take displaced workers --

08:08

folks who are affected by COVID --

08:09

connect them to money and services and new jobs.

08:14

We took a high-level senior software engineer

08:16

who could architect the full platform

08:19

and then apprentices who could execute on that roadmap,

08:22

and in 11 days,

08:24

we had a functioning prototype.

08:27

You see, the local mom-and-pop,

08:30

the school district,

08:31

the regional manufacturer,

08:33

they all have software needs, and they’re going to pay someone to do it.

08:37

With this model,

08:39

they can have their solutions delivered back to them,

08:42

but also participate

08:45

in the creation of high-growth, high-wage jobs in their area.

08:49

The last ingredient in our recipe is community.

08:54

We need vibrant spaces

08:56

that meet the aspirations of technologists and entrepreneurs,

09:00

so we build castles for the underdogs.

09:04

We buy blighted buildings in our downtowns for pennies on the dollar,

09:09

improve them,

09:10

lease them back out to ourselves and others in the technology industry.

09:14

This creates community around the idea of leveling up entry-level humans

09:19

and builds a shared understanding

09:21

and value around what it means to have access to unlimited talent.

09:26

The first project that we did

09:28

was a building that had stood empty for 40 years before we took it over.

09:33

We showed up with our tenant list and our ability to do work.

09:36

Our partner showed up with a building that was empty and decaying.

09:42

We painted the walls,

09:43

we built a bunch of desks,

09:44

we hung a lot of TVs,

09:47

and when the coffee shop opened at the front of that building,

09:50

it was like someone had flipped a switch on that corner of downtown.

09:55

Suddenly, there were a thousand students and tenants

09:58

and community members visiting that building each day.

10:03

These ingredients,

10:05

when you take them all together,

10:08

they produce real impact driven by real change

10:13

that affect real people

10:15

who have names and faces and families and pets.

10:21

Just one quick example.

10:22

Our pal, Miguel,

10:24

who was once incarcerated,

10:26

he didn’t have any prospects for his future,

10:28

his professional life or really, his family.

10:31

He was scholarshiped through our pre-apprenticeship program

10:34

using government dollars.

10:36

Miguel veered just to the left of computer programming,

10:39

landed neck-deep in analytics and website funnels.

10:44

He apprenticed for our digital marketing program.

10:48

Eighteen months later,

10:49

Miguel has a full-time job,

10:52

a great salary,

10:53

benefits and a matching 401(k).

10:57

We’ve worked with over 5,000 students,

11:00

and of those entering our career programs,

11:03

over 80 percent earn technical employment.

11:06

And in Fresno, this means that the new technology workforce

11:09

is greater than 50 percent female or gender nonconforming,

11:14

greater than 50 percent minority or Latinx

11:16

and 20 percent first-generation.

11:19

And those demographics mirror the demographics of our county.

11:24

These are folks leaving restaurant, retail, factory and field labor,

11:28

earning on average less than 20,000 dollars a year,

11:32

exiting the programs earning 60-80,0000 dollars a year.

11:37

That’s gas in the tank and rent paid on time.

11:41

And when you do that enough times,

11:44

you see more sandwiches being purchased at the local panini shop;

11:49

newer, more reliable cars taking these folks to work;

11:54

the tax base improving,

11:55

which invests in schools and rebuilds roads;

11:58

homes in those communities that are being built or bought

12:01

by the people who are actually going to live in them;

12:05

dilapidated buildings that once stood empty

12:08

now full of energized underdogs sipping coffee and writing code

12:13

and, most importantly,

12:15

bringing with them the next generation of human

12:18

that didn’t see themselves leaving the packing house

12:21

until they saw their pal make it work.

12:24

And we can do this.

12:26

You know, it's not at all a mystery,

12:28

especially now that we’ve spent 10 minutes talking about it.

12:33

But we do have to do three very specific and deliberate things.

12:39

Invite the underdog in the front door;

12:43

pay them to learn like it’s their job;

12:46

and then build them castles in their hometowns.

12:50

It’s worked in Fresno,

12:51

it’s working in Bakersfield and Toledo, Ohio

12:54

and it can work in underestimated cities all over the world.

12:59

Thank you so much for your attention.

13:01

(Applause)


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