[MUSIC PLAYING]
SARA MENKER: I actually have a gift for everyone.
So I want you guys to pass this around and just take
one tortilla chip.
I'm gonna-- [LAUGHING] you're not allowed to have more than
one.
And don't eat it.
[LAUGHING] Sorry. [LAUGHING] It'll
defeat the purpose of what I'm trying to achieve.
I guess, as it's being passed around,
I want you guys to have that tortilla chip as a way
to contextualize the conversation I want us all
to have.
But, you know, show of hands, just for the sake
of show of hands, who has a smartphone of any kind?
100% of the room.
So a decade ago, smartphones that we have today,
with the functionality that we have,
would have been over two times expensive, if not more.
So we've managed to halve the price of the devices we use.
The tortilla chip in your hand, however,
has gone up by 2 and 1/2 times.
So, the one thing we need to live with and survive with
is the one thing that we've managed to not solve for.
And, you know, how expensive food has gotten and will
continue to get is a very scary proposition.
So, by way of background, I'm Ethiopian.
I was raised in Ethiopia in the '80s
at the peak of the food crisis.
So my natural transition was to become a commodities trader
on Wall Street.
Now, in 2008, there was something
that happened in the commodities markets.
Oil hit $150 and global food prices and grain prices
hit what was an all-time high.
At that point in time, I freaked out.
And my freaking out was really driven by a fear
that 20 years down the line, there's
no way we're going to be able to afford food, personally.
So, I just kind of developed an obsession around agriculture.
I was trading energy at the time.
So, there wasn't a reason why I should
be obsessed with this problem, other than I knew the markets.
And that obsession eventually led me to quit my job in 2012
to go down this path of trying to help
make food cheap and abundant.
Now when I left, it was the beginning of 2012,
and what I didn't know was later that year,
we'd hit new highs in grain prices.
And little did I know that the country I'd chosen to move to,
which is Kenya-- so, I'd left New York and moved to Kenya--
would be hit particularly hard by it.
See, what happened in 2012 is in 2011,
Kenya had experienced a drought that it was barely
recovering from.
And as it was starting to recover,
a disease had been discovered, a crop disease
had been discovered.
And just as that disease started to spread,
droughts in the US and Russia hit simultaneously.
Now, the US and Russia are two of the world's largest
grain exporting countries.
What that meant was that Kenyan corn prices
went from being below the global average to being
well above the average.
And it kept getting worse because what happened
was policymakers then adopted a policy of reinstating
a 50% import tax on corn.
Now, corn in the US might mean something
like tortilla chips, high fructose corn syrup, ethanol.
Corn in Kenya is what Kenyans eat every single day.
It provides almost half the calorie requirements
for the country.
And the poorest quarter in the country spend about 1/3
of their income on that one grain.
Now, when I graduated from college
I remember being told to expect to spend about 1/3 of my salary
on rent.
So, now imagine spending 1/3 of your total income,
not on all your food requirements,
but on a single good.
See, there's no more room for error
in the way we think about food and the way
we think about food prices.
And we're already at a stage when we have no room for error.
And errors like the one I gave as an example in Kenya,
unfortunately, happen all over Africa.
And it happens not because policymakers
are malicious or malintentioned.
It happens because there's a lack of information and clarity
in the type of information needed
to make the policy decisions being made.
Now, fast forward and where we're going as a world.
We're expected to be 9.1 billion people by 2050.
As a wealthier population, we're expected
to need 70% more food because we consume a lot more than we're
actually growing as a population.
So in a space where there's no room for error,
you have this happening with population.
And to top it off, you have climate change.
The frequency of droughts, floods, wind-related crop
damage has grown exponentially, such that actually figuring out
agricultural production and productivity
has become impossible.
So, what happens next?
It's clear we have to increase food production.
How does that happen?
It's going to happen in one of two ways.
We're either going to have to start
growing more food on the plots of land
that we're growing food in, so increasing yield.
Or we're going to have to plant new land.
Now, I'll argue-- as much as I said about Africa-- that Africa
is actually the place for that to happen.
Not only does it have the largest swath of arable land
remaining in the world-- four times
the amount planted across the US today-- it also
has such low yields that if, in 2012, African corn yields were
just equivalent to the world average,
we could have actually increased corn
production by 12% that year.
It was a 2% reduction in corn production
globally that actually led to that sort of price spike.
So imagine what a 12% increase in production can happen.
So, the next question you ask is, so how
are we going to solve this mess?
And how do we go about doing that?
That's where we like to think of the work
that we're doing as being that moonshot around how
to address productivity in Africa.
We've created a real time, basically,
tracking application that allows all sorts of users,
from policymakers to traders, to basically visualize and analyze
all sorts of data around agriculture.
So the application currently, basically-- it's
not that the data isn't available, by the way,
in terms of what policy makers are using.
It's that it's highly disaggregated,
it comes in multiple formats, and nobody knows where it sits.
So, what ends up happening is that a user
doesn't know which source to tap for what piece of information.
So, what we've done is we've created a blank slate.
It's a blank slate that actually sucks in and aggregates
all sorts of data, over 40,000 indicators, that basically look
at all sorts of analytics around Africa, African agriculture,
but also global agriculture.
So metrics from emissions to rainfall to vegetation
to production.
So what ends up happening is a continent where 30% of its GDP
comes from agriculture, less than 1%
of outstanding bank loans today are to the agricultural sector.
The reason is capital's not available.
So what we are trying to do is basically
make the data available that lets capital be unleashed.
So, in this context now, you take your tortilla chips
and I want everybody to imagine being a credit
officer for farmers in Nigeria.
And you have a Nigerian cassava farmer
that comes to your office.
In the past, the way decisions are made is a yes or no.
And oftentimes, it ends with a no.
What we're envisioning is a future
where, if you are a credit officer,
you have a tool that lets you go in and look
at all sorts of parameters around, say, supply
and the supply of certain different crops
that are important in Nigeria.
So to give an example, tomatoes.
Nigerians are actually one of the largest importers
of tomato paste.
And wheat.
And so it allows you to go in and create this real time
tracking system that allows you to look
at a series of these indicators in such a way
that you can generate a series of charts.
Now, I've kind of fast-tracked this process
so that we can get to what we actually track.
So, we track everything from-- so imagine
you have a cassava farmer coming in for a loan.
Now, cassava to Nigerians is what's
equivalent to the corn story that I told you about in Kenya.
They eat it every single day.
Now as you can see, cassava production,
if you're a credit officer, you have
to understand the volatility in production cycles.
Now you'll see, in 2009 there was a cassava crisis.
You'll want to understand why that happened
and how you can think of that differently.
That actually happened because there
was flooding in the core cassava-producing regions
of Nigeria at the time.
Now what ends up happening when you have that sort of rainfall
and that sort of drop in cassava production is you
have a drastic import of wheat.
Wheat is the alternative to it.
And the US provides 80% of the wheat into Nigeria.
And you can track vegetation, so you can actually
watch how vegetation in a particular region evolves.
By being able to look at and dynamically visualize
all this data that is incredibly complex,
you know, no one-- this is not coming from a single source.
Each one looks at various sources
to extrapolate what the true number should be because no one
source actually tells us what it is.
And so, what we're hoping is that this tool
becomes the tool that allows us to basically unleash capital
into African agricultural markets.
To increase the yield so that farmers can basically
produce more on the existing plots of land, which then feeds
into more investment going into all that other arable land
that I discussed, which then leads
to abundant food and hopefully cheap food.
Thanks.
[APPLAUSE]
FEMALE SPEAKER: OK.
So quick question for Sara before you go down,
and then we'll take the next one up.
So we're going to go to brainstorming,
and there's going to be two tables who
are going to work on this idea.
So in addition to all the ideas that they think of,
whatever they think of, 2/3 "yes, and"--
what kind of distro, technology, partners, money, other ideas
that you guys can think of that would help this idea.
And then 1/3 "yes, but," meaning like what are your critiques
and challenges that you think are there,
and some ideas there.
Is there any set of things, one or two things, that you really
need help on right now that you know as you're building?
SARA MENKER: As we're building?
Well, there's two things I can think of.
One is we just actually finished our first fundraise,
and we're out there hiring all sorts of talent, from front end
to back end to machine learning engineers--
FEMALE SPEAKER: So people.
SARA MENKER: We need people.
And the second is the tool that I
showed you which is the visualization
around satellite imagery and weather data-- everything
we've done is basically allow it to be animatable.
So, a user gets to curate their experience
and get to zoom into a particular region.
And then say, I want to understand the 10 day
average in this particular region versus the 10 day
average there.
And so, when you're running a series of computations
on images that are coming in daily
or are varying anywhere from being 50 meters squared to,
you know, 100 meters square, you run
into all sorts of challenges, in terms of giving it to the user.
So we're thinking about ways of optimizing that further.
FEMALE SPEAKER: So mimplifying-
SARA MENKER: Yes, simplifying that process.
FEMALE SPEAKER: --figure out how to interact.
OK.
SARA MENKER: Yeah, so we're on geospacial.
Yeah.
FEMALE SPEAKER: OK, super.
All right.
Thank you.