0:00:00.513,0:00:05.007
So data and analytics are dramatically[br]changing our everyday lives.

0:00:05.566,0:00:06.739
Not just online,

0:00:06.763,0:00:09.097
not just in some distant future,

0:00:09.121,0:00:10.443
but in the physical world,

0:00:10.467,0:00:13.236
and in very real and tangible ways.

0:00:13.897,0:00:18.102
I spent the past 11 years[br]of my life as a geek at MIT,

0:00:18.126,0:00:19.532
working in big data labs

0:00:19.556,0:00:23.332
that seek to use data science[br]to study the physical world

0:00:23.356,0:00:25.530
and try to solve society's great problems.

0:00:26.985,0:00:30.921
The field of big data seeks to analyze[br]massive pools of data

0:00:30.945,0:00:34.865
using computational tools[br]to find patterns and trends.

0:00:35.561,0:00:38.576
Data can be a really[br]extraordinary storyteller,

0:00:38.600,0:00:41.570
unveiling the hidden narratives[br]of things in our everyday lives

0:00:41.594,0:00:43.060
that we never would have seen.

0:00:43.439,0:00:47.236
I find the personal stories of inanimate[br]things brought to life

0:00:47.260,0:00:48.932
to be extraordinarily compelling.

0:00:49.853,0:00:52.853
I want to highlight, first,[br]two projects from my time at MIT

0:00:52.877,0:00:55.430
that I think highlight[br]this phenomenon really well.

0:00:56.074,0:00:58.139
The first is called Trash Track,

0:00:58.163,0:01:02.140
and in this project, we sought to better[br]understand the waste-management system,

0:01:02.164,0:01:03.854
to answer the question

0:01:03.878,0:01:06.317
"Where does your trash go[br]when you throw it away?"

0:01:06.341,0:01:08.851
Your old coffee cup or that flip phone

0:01:08.875,0:01:11.304
that you carried around[br]in the early 2000s,

0:01:11.328,0:01:14.575
or a bagel or this morning's paper --

0:01:14.599,0:01:16.163
where do these things go?

0:01:16.652,0:01:19.533
This data didn't exist,[br]so we had to create it.

0:01:20.251,0:01:23.419
We answered and then[br]visualized this question

0:01:23.443,0:01:26.940
by installing small sensors[br]into pieces of trash

0:01:26.964,0:01:29.060
and then throwing them[br]into the waste system.

0:01:29.601,0:01:32.400
And what you're seeing here is the data.

0:01:32.903,0:01:35.679
Every line, every node that you see

0:01:35.703,0:01:39.175
is a single piece of trash[br]moving through the city of Seattle,

0:01:39.199,0:01:41.505
and then across the state,

0:01:41.529,0:01:43.180
and then across the country,

0:01:43.204,0:01:44.946
as weeks and months go by.

0:01:45.606,0:01:47.671
And it's important to visualize this data,

0:01:47.695,0:01:50.367
because none of you[br]are, probably, sitting here thinking,

0:01:50.391,0:01:51.649
"Yeah, that looks right."

0:01:51.673,0:01:53.551
(Laughter)

0:01:53.575,0:01:55.433
"That's working like it should, right?"

0:01:55.457,0:01:56.619
Because, no --

0:01:56.643,0:01:58.113
(Laughter)

0:01:58.582,0:02:02.615
What the data shows us[br]is a highly inefficient system

0:02:02.639,0:02:06.484
whose inherent brokenness[br]I don't think we really would have seen

0:02:06.508,0:02:09.389
had the sensors not done[br]the journalism for us.

0:02:10.597,0:02:13.247
A second project[br]that I'd have to highlight

0:02:13.271,0:02:17.929
has to do with creating robots[br]that dive into sewers

0:02:17.953,0:02:19.574
and sample wastewater.

0:02:20.306,0:02:23.001
I know that sewage kind of gets a bad rap,

0:02:23.025,0:02:24.870
but it's actually kind of awesome,

0:02:24.894,0:02:26.895
because it can tell us[br]an incredible amount

0:02:26.919,0:02:28.659
about the health of our communities.

0:02:28.683,0:02:31.934
This technology was spun out[br]by a group call Biobot Analytics,

0:02:31.958,0:02:34.530
who's creating a cutting-edge technology

0:02:34.554,0:02:38.717
to turn our sewers into modern-day[br]health observatories.

0:02:38.741,0:02:42.097
Their goal is to study opioids[br]within the sewage

0:02:42.121,0:02:44.756
to better understand[br]consumption in cities.

0:02:44.780,0:02:46.718
And this data is key,

0:02:46.742,0:02:49.836
because it really helps cities understand[br]where people are using,

0:02:49.860,0:02:51.734
how to allocate resources

0:02:51.758,0:02:54.799
and the effectiveness[br]of programming over time.

0:02:55.502,0:02:58.419
Once again, the technology[br]that's built into this machine

0:02:58.443,0:03:00.284
is pulling back the curtain

0:03:00.308,0:03:04.368
and showing us something about our cities[br]that we never would have seen without it.

0:03:04.392,0:03:06.926
So it turns out, as we see,

0:03:06.950,0:03:09.472
that big data is really everywhere --

0:03:09.496,0:03:10.739
even in your toilet.

0:03:11.318,0:03:14.906
And so now that we've talked[br]about trash and sewage,

0:03:14.930,0:03:16.301
let's move on ...

0:03:16.325,0:03:17.533
to food.

0:03:17.557,0:03:18.708
(Laughter)

0:03:18.732,0:03:22.388
A year ago, I left MIT[br]to pursue a passion in food,

0:03:22.412,0:03:23.653
and in 2017,

0:03:23.677,0:03:26.610
started a company with my husband,[br]called Family Dinner.

0:03:26.634,0:03:30.747
The goal of our company[br]is to create community around local food

0:03:30.771,0:03:32.716
and the people who grow it.

0:03:32.740,0:03:35.277
To make this happen,[br]we're using data analytics,

0:03:35.301,0:03:37.534
automation and technology

0:03:37.558,0:03:40.296
to build a distributed[br]network of local farms

0:03:40.320,0:03:42.582
and to make improvements[br]on the food system.

0:03:43.187,0:03:44.839
So what we see here

0:03:44.863,0:03:48.347
is that the broad techniques[br]and the mission of what we're trying to do

0:03:48.371,0:03:51.473
is really not dissimilar[br]from the work at the MIT labs.

0:03:52.309,0:03:54.985
Which brings us to a critical question:

0:03:55.009,0:03:59.029
Why exactly would someone[br]leave a very promising career

0:03:59.053,0:04:03.246
at one of the top[br]urban science labs in the world

0:04:03.270,0:04:05.923
to drive carrots around[br]in her mom's Acura?

0:04:05.947,0:04:07.687
(Laughter)

0:04:08.241,0:04:09.437
It's a great car.

0:04:10.496,0:04:13.260
Because I believe[br]that the story of local food

0:04:13.284,0:04:16.776
needs to be understood, told and elevated,

0:04:16.800,0:04:17.951
and in many ways,

0:04:17.975,0:04:21.755
I think that nerds like us[br]are really uniquely poised to tell it.

0:04:22.304,0:04:23.834
So where are we starting?

0:04:23.858,0:04:25.361
What's our starting point?

0:04:25.775,0:04:30.268
The current national food system[br]is optimized for one thing only,

0:04:30.292,0:04:32.760
and that's corporate profit, right?

0:04:32.784,0:04:34.034
And think about that.

0:04:34.058,0:04:37.441
The most compelling reason[br]for food companies to exist

0:04:37.465,0:04:39.419
is not to feed hungry people,

0:04:39.443,0:04:41.359
it's not to make delicious-tasting food.

0:04:41.922,0:04:43.072
It's profit.

0:04:43.706,0:04:47.203
And that has detrimental effects[br]at all levels of our food system.

0:04:47.778,0:04:50.786
The antibiotics and pesticides[br]that are being put into our food

0:04:50.810,0:04:52.824
are detrimental to our health.

0:04:52.848,0:04:55.986
Price pressure is forcing[br]small farms out of business.

0:04:56.010,0:04:58.628
In fact, a lot of the things[br]that you think about farms

0:04:58.652,0:04:59.808
no longer exist.

0:04:59.832,0:05:03.174
Farms don't look like farms,[br]they look like factories.

0:05:03.198,0:05:04.437
And at the end of the day,

0:05:04.461,0:05:07.467
the quality of the food[br]that we're eating really suffers, too.

0:05:07.935,0:05:11.871
A factory-farm tomato[br]may kind of look like a regular tomato:

0:05:11.895,0:05:13.556
bright red exterior ...

0:05:13.580,0:05:14.996
But when you bite into it,

0:05:15.020,0:05:17.570
the taste and texture[br]just leave you wanting.

0:05:18.651,0:05:21.804
And we know that perhaps[br]the greatest tragedy in all of this

0:05:21.828,0:05:25.860
is that between 30 and 40 percent[br]of this food is just wasted ...

0:05:26.727,0:05:28.109
thrown away.

0:05:28.133,0:05:30.808
That is 1.6 billion tons.

0:05:30.832,0:05:33.260
I can't even wrap my head[br]around that number.

0:05:33.284,0:05:35.561
1.6 billion tons.

0:05:35.585,0:05:39.474
That's 1.2 trillion dollars a year

0:05:39.498,0:05:40.771
in wasted food.

0:05:41.526,0:05:43.634
That is the cost of on-demand eating

0:05:43.658,0:05:44.809
and convenience

0:05:44.833,0:05:46.600
and the broken food system.

0:05:47.220,0:05:48.879
Now, where's this waste happening?

0:05:48.903,0:05:50.944
Where's all this waste coming from?

0:05:50.968,0:05:52.968
Well, we know that it happens in the field

0:05:52.992,0:05:55.365
when you don't pick[br]the sexiest-looking potatoes.

0:05:55.389,0:05:57.635
We know that it happens in transit,

0:05:57.659,0:05:59.023
at the warehouses,

0:05:59.047,0:06:00.699
in the grocery stores.

0:06:00.723,0:06:03.155
And finally, on our own kitchen counters,

0:06:03.179,0:06:07.658
when we determine that that spotty,[br]brown banana no longer looks so yummy.

0:06:08.238,0:06:10.333
All that waste, all that effort.

0:06:10.871,0:06:12.475
Food is planted,

0:06:12.499,0:06:15.047
grown, harvested, shipped,

0:06:15.071,0:06:17.459
and then just thrown away.

0:06:18.680,0:06:21.052
We think that there has to be[br]a better way.

0:06:22.295,0:06:24.002
And so how to we improve upon this?

0:06:24.026,0:06:26.171
How do we make a better system?

0:06:26.601,0:06:27.839
In order to do this,

0:06:27.863,0:06:30.459
we understand that we need[br]to eliminate waste

0:06:30.483,0:06:32.129
in the food supply chain.

0:06:32.652,0:06:34.962
We need to get data[br]in the hands of farmers,

0:06:34.986,0:06:36.940
so that they can make better predictions.

0:06:36.964,0:06:39.978
So they can, you know,[br]kind of compete with the big guy.

0:06:40.002,0:06:41.209
And then finally,

0:06:41.233,0:06:43.382
we need to prize, as a company,

0:06:43.406,0:06:46.232
quality and taste above everything,

0:06:46.256,0:06:49.386
so that people really value[br]the delicious food on their plates.

0:06:50.493,0:06:53.025
This, we believe, is the better system.

0:06:53.049,0:06:54.512
This is the better way.

0:06:54.830,0:06:58.315
And the path to that better way[br]is paved with data.

0:06:59.292,0:07:02.942
To highlight all of this,[br]I want to tell the tale of two tomatoes.

0:07:03.791,0:07:05.691
We'll talk about them one by one.

0:07:06.120,0:07:09.303
A tomato in itself contains[br]a beautiful snapshot

0:07:09.327,0:07:13.113
of everything you might want to know[br]about the life cycle of that fruit:

0:07:13.137,0:07:15.288
where it was grown,[br]what it was treated with,

0:07:15.312,0:07:16.631
nutritional value,

0:07:16.655,0:07:18.415
miles traveled to get to your plate,

0:07:18.439,0:07:20.525
CO2 emissions along the way.

0:07:20.549,0:07:22.169
All of that information,

0:07:22.193,0:07:24.811
all those little chapters[br]in one small fruit.

0:07:25.215,0:07:26.376
It's very exciting.

0:07:26.814,0:07:29.279
This is tomato number one.

0:07:29.303,0:07:32.747
This is the guy that you'll find[br]in sub shops, supermarkets

0:07:32.771,0:07:34.734
and fast-food joints around the world.

0:07:35.137,0:07:38.377
It's got a really long[br]and complicated backstory.

0:07:38.968,0:07:43.360
It's been treated with a cocktail[br]of, like, a dozen pesticides

0:07:43.384,0:07:47.614
and it has traveled at least[br]1,600 miles to get to your house.

0:07:48.316,0:07:50.031
And the image here is green,

0:07:50.055,0:07:53.672
because these tomatoes are picked[br]when green and hard as a rock,

0:07:53.696,0:07:56.057
and then they are gassed along the way

0:07:56.081,0:07:58.238
so that when they arrive[br]at the destination,

0:07:58.262,0:08:01.198
they look bright and shiny[br]and red and ripe.

0:08:02.284,0:08:04.213
All of that effort,

0:08:04.237,0:08:07.578
all of that agricultural[br]innovation and technology

0:08:07.602,0:08:11.606
to create a product[br]that is entirely without taste.

0:08:12.388,0:08:14.583
And onto the second tomato in our tale.

0:08:14.607,0:08:16.923
This is the local version of the fruit.

0:08:16.947,0:08:19.055
Its story is much, much shorter.

0:08:19.794,0:08:23.744
This guy was grown by Luke Mahoney[br]and his family at Brookford Farm

0:08:23.768,0:08:25.452
in Canterbury, New Hampshire.

0:08:26.028,0:08:28.046
It's got a pretty boring backstory.

0:08:28.567,0:08:30.072
It was planted,

0:08:30.096,0:08:31.548
sat in the sun

0:08:31.572,0:08:32.863
and then it was picked.

0:08:32.887,0:08:34.108
(Laughter)

0:08:34.457,0:08:35.615
That's it.

0:08:35.639,0:08:37.099
Like, you wouldn't want to --

0:08:37.123,0:08:38.889
yeah, there's not much more to that.

0:08:38.913,0:08:42.529
And it traveled maybe 70 miles[br]to get your plate.

0:08:42.553,0:08:44.386
But the difference is dramatic.

0:08:44.797,0:08:48.090
I want you think about the last time[br]you ate a fresh, summer tomato.

0:08:48.114,0:08:50.163
And I know we're all[br]covered in our jackets,

0:08:50.187,0:08:51.338
but think about it.

0:08:51.362,0:08:53.554
The last time you ate[br]a tomato from the garden.

0:08:53.578,0:08:55.307
It's warm from the sun,

0:08:55.331,0:08:56.504
it's richly red,

0:08:56.528,0:08:58.332
maybe it smells like dirt.

0:08:58.356,0:09:01.609
There's something nostalgic[br]and almost magical in that experience.

0:09:02.175,0:09:05.266
The taste and the flavor are incomparable.

0:09:05.991,0:09:09.439
And we really don't have to travel[br]super far to get it.

0:09:10.812,0:09:13.376
Now this story extends up the food chain,

0:09:13.400,0:09:16.114
from the fruits and the vegetables[br]that are on our plate

0:09:16.138,0:09:18.954
to the animals and the animal[br]products that we consume.

0:09:19.545,0:09:21.242
What goes into raising them,

0:09:21.266,0:09:25.236
and more importantly,[br]what doesn't go into raising them,

0:09:25.260,0:09:26.701
is critically important.

0:09:28.006,0:09:30.071
Luke and his family have 60 cows.

0:09:30.744,0:09:32.126
They use traditional methods.

0:09:32.150,0:09:33.773
They do it the old way:

0:09:33.797,0:09:35.215
pasture-raised,

0:09:35.239,0:09:37.541
no hormones, no antibiotics,

0:09:37.565,0:09:38.870
hay for days.

0:09:39.707,0:09:43.763
And what they're doing here[br]is just treating cows like they're cows,

0:09:43.787,0:09:46.055
not like they're in a science experiment.

0:09:46.079,0:09:48.721
He's raising animals the way[br]that his grandfather

0:09:48.745,0:09:50.586
and his grandfather would have.

0:09:50.610,0:09:52.740
And at the end, it's just better.

0:09:52.764,0:09:54.098
It's better for the animals;

0:09:54.122,0:09:55.655
it's better for the environment.

0:09:55.679,0:09:58.042
Luke is not optimizing[br]for profit or price,

0:09:58.066,0:10:00.208
but for taste and for humanity.

0:10:01.257,0:10:04.524
And what you're thinking is,[br]"There's already a solution to this.

0:10:04.548,0:10:06.238
It's the farmer's markets."

0:10:06.262,0:10:07.747
The ones that many of you visit

0:10:07.771,0:10:09.600
and the ones that I really enjoy.

0:10:10.279,0:10:13.747
They are a wonderful, but,[br]in many ways, suboptimal solution.

0:10:14.294,0:10:16.802
For us as the consumers,[br]it's kind of great, right?

0:10:16.826,0:10:18.057
You go,

0:10:18.081,0:10:20.010
there's this beautiful bounty of food,

0:10:20.034,0:10:23.283
you get the warm and fuzzies[br]for supporting a local farm

0:10:23.307,0:10:27.536
and you get the experience of trying[br]something new and trying diverse products.

0:10:27.560,0:10:30.042
And inevitably, there's some guy[br]playing the ukulele

0:10:30.066,0:10:31.415
somewhere in the background.

0:10:31.439,0:10:32.439
(Laughter)

0:10:33.518,0:10:37.138
But for the farmers,[br]this presents a lot of risk, right?

0:10:37.162,0:10:38.313
You wake up at four.

0:10:38.337,0:10:40.343
You pack your truck, you hire a team,

0:10:40.367,0:10:41.535
you get to your stall,

0:10:41.559,0:10:43.527
but you have no guarantees

0:10:43.551,0:10:45.897
that you're going to move[br]your product that day.

0:10:45.921,0:10:47.946
There's too many variables in New England.

0:10:47.970,0:10:50.234
For example, the weather,

0:10:50.258,0:10:53.028
which is just, like,[br]a little bit unpredictable here.

0:10:53.680,0:10:55.656
The weather is one of the many X factors

0:10:55.680,0:10:59.941
that determine whether or not[br]a market will be worth it for the farmers.

0:11:01.101,0:11:03.112
Every time, they roll the dice.

0:11:03.647,0:11:05.504
And there's another option.

0:11:05.528,0:11:07.684
Here, we're talking about CSAs:

0:11:07.708,0:11:09.960
community-supported agriculture.

0:11:09.984,0:11:12.500
In this model, customers pay up front,

0:11:12.524,0:11:14.731
bearing the financial risk for the farms.

0:11:14.755,0:11:16.424
Farmers grow what they can

0:11:16.448,0:11:18.790
and the customers enjoy that bounty.

0:11:19.176,0:11:20.863
This also has a couple issues.

0:11:20.887,0:11:22.230
It's great for the farmer,

0:11:22.254,0:11:24.922
because they're ensuring[br]that they'll sell what they buy,

0:11:24.946,0:11:26.196
but for us,

0:11:26.220,0:11:28.251
we still have to go[br]and pick up that share,

0:11:28.275,0:11:31.668
and we know that a lot of farms[br]can't grow a huge diversity of products,

0:11:31.692,0:11:35.297
so sometimes, you're stuck with a mountain[br]of any one particular thing.

0:11:35.845,0:11:37.723
Maybe this has happened to some of you.

0:11:38.217,0:11:42.326
And what do you do with 25 pounds[br]of rutabaga in the dead of winter?

0:11:42.350,0:11:43.716
I still don't know.

0:11:45.346,0:11:47.155
So back to the question.

0:11:47.179,0:11:48.784
How do we fix this?

0:11:48.808,0:11:51.390
What we're hoping to do[br]and what we're hoping to build

0:11:51.414,0:11:53.404
is just a better way to CSA.

0:11:54.274,0:11:58.898
And there are three core innovations[br]that make this thing hum.

0:11:59.448,0:12:00.618
The first of which

0:12:00.642,0:12:03.548
is a subscription-based[br]e-commerce platform,

0:12:03.572,0:12:06.376
which helps us create[br]a consistent demand for our farmers

0:12:06.400,0:12:07.897
throughout the year.

0:12:07.921,0:12:09.939
The subscription part here is key.

0:12:09.963,0:12:11.565
Orders process weekly,

0:12:11.589,0:12:14.011
customers opt out instead of opt in --

0:12:14.035,0:12:17.302
that means we've got kind of the same[br]number of orders week to week.

0:12:17.619,0:12:21.546
Second, this means[br]that if farmers can sell online,

0:12:21.570,0:12:25.155
they're no longer limited to the geography[br]directly around their farm

0:12:25.179,0:12:27.525
or to the number of markets[br]that they can sell.

0:12:27.549,0:12:29.868
We've blown the doors[br]off of that for them.

0:12:30.791,0:12:32.784
Second: demand forecasting.

0:12:32.808,0:12:35.809
We're using analytics to allow[br]ourselves to look into the future

0:12:35.833,0:12:37.411
and forecast demand.

0:12:37.435,0:12:40.673
This lets farmers know[br]how much to harvest in the near-term,

0:12:40.697,0:12:42.862
but also what to plant going forward.

0:12:43.537,0:12:46.176
If 200 orders process on Monday,

0:12:46.200,0:12:48.295
then we buy to meet that exact demand.

0:12:48.617,0:12:49.935
200 heads of broccoli,

0:12:49.959,0:12:52.316
200 pieces of salmon, [br]et cetera, et cetera.

0:12:52.980,0:12:54.457
This automation in ordering

0:12:54.481,0:12:58.615
means that here, we are eliminating[br]the waste in the food system

0:12:58.639,0:13:00.621
that bothers us all so much,

0:13:00.645,0:13:04.773
because we are ensuring that the supply[br]meets the exact demand.

0:13:05.616,0:13:08.331
It also allows us to look[br]into the future with the farmers

0:13:08.355,0:13:09.664
and do crop planning.

0:13:09.688,0:13:11.934
So if we can say to them,[br]in June of this year,

0:13:11.958,0:13:14.487
"I'm going to need 400 pounds of asparagus

0:13:14.511,0:13:17.069
and 500 pounds of berries every week,"

0:13:17.093,0:13:18.958
they can plant that accordingly,

0:13:18.982,0:13:21.010
knowing with confidence[br]that they will sell

0:13:21.034,0:13:22.579
everything that they have grown.

0:13:22.603,0:13:25.246
And finally, we use[br]a route-optimization software

0:13:25.270,0:13:28.073
to help us solve the problem[br]of the traveling salesman.

0:13:28.097,0:13:31.714
We get a fleet of workers to come in[br]and help us go the last mile,

0:13:31.738,0:13:34.298
bringing all these goodies[br]directly to your door.

0:13:34.322,0:13:35.700
Without data science

0:13:35.724,0:13:38.315
and a super-capable, wonderful team,

0:13:38.339,0:13:40.044
none of this would be possible.

0:13:40.800,0:13:42.450
So maybe you've seen

0:13:42.474,0:13:45.827
that we've got some sort of fiery,[br]passionate core beliefs.

0:13:45.851,0:13:48.498
Yes, we're trying to build[br]a sustainable business,

0:13:48.522,0:13:50.617
but our eye is not only on profit,

0:13:50.641,0:13:53.450
it's on building a better,[br]holistic system of food.

0:13:54.421,0:13:55.794
And here's what we value.

0:13:56.290,0:13:57.690
People first.

0:13:57.714,0:13:59.763
We're trying to build[br]community around food,

0:13:59.787,0:14:02.316
the people who love it[br]and the people who grow it.

0:14:02.340,0:14:04.974
We built this company[br]to support small farms.

0:14:05.900,0:14:07.090
Zero waste.

0:14:07.114,0:14:09.732
We all hate wasting food,[br]it just feels wrong --

0:14:09.756,0:14:11.078
even that weirdo banana

0:14:11.102,0:14:14.059
that's been sitting around[br]on your coffee table for too long.

0:14:14.083,0:14:15.962
And lastly, taste.

0:14:16.451,0:14:17.832
If it doesn't taste good,

0:14:17.856,0:14:20.873
if it's not that, like,[br]perfect summer tomato,

0:14:20.897,0:14:22.047
why bother?

0:14:22.803,0:14:25.387
So what we've done[br]is worked with all these local farms

0:14:25.411,0:14:26.793
to bring their things in

0:14:26.817,0:14:29.057
and then to drop them[br]directly at your door,

0:14:29.081,0:14:31.113
so that we're connecting you right to them

0:14:31.137,0:14:33.578
and making, again, a more holistic system.

0:14:34.252,0:14:36.365
This is our vision of the future.

0:14:36.389,0:14:39.857
To extend this model beyond Boston,[br]beyond New England

0:14:39.881,0:14:41.620
and across the country.

0:14:41.644,0:14:45.734
To create a nationwide[br]distributed network of local farms

0:14:45.758,0:14:47.557
and to connect all these farmers

0:14:47.581,0:14:50.167
with the people like you[br]who will love their food.

0:14:51.602,0:14:53.286
We believe, at the end of the day,

0:14:53.310,0:14:57.753
that really insisting on eating local food[br]is a revolutionary act.

0:14:58.213,0:14:59.741
And we invite you to join us.

0:15:00.311,0:15:01.604
And who knows?

0:15:01.628,0:15:04.637
You may even make[br]some friends along the way.

0:15:05.611,0:15:06.761
Thank you very much.

0:15:06.785,0:15:07.935
(Applause)