For the people most immersed in the tech sector, it’s hard to think of a more controversial topic than the ultimate impact of artificial intelligence (AI) on society.
By eventually empowering machines with a level of superintelligence, there are many different possible outcomes ranging from Kurzweil’s technological singularity to the more dire predictions popularized by Elon Musk.
Despite this wide gap in potential outcomes, most technologists do agree on one thing: AI will have a profound impact on the society and the way we do business.
The Economic Impact of AI
Today’s infographic comes from the Extraordinary Future 2017, a new conference in Vancouver, BC that focuses on emerging technologies such as AI, autonomous vehicles, fintech, and blockchain tech.
In the below infographic, we look recent projections from PwC and Accenture regarding AI’s economic impact, as well as the industries and countries that will be the most profoundly affected.
According to PwC’s most recent report on the topic, the impact of artificial intelligence (AI) will be transformative.
By 2030, AI is expected to provide a $15.7 trillion boost to GDP worldwide – the equivalent of adding 13 new Australias to the global economy.
A Geographic Breakdown
Where will AI’s impact be most pronounced?
According to PwC, China will be the region receiving the most economic benefit ($7.0 trillion) from AI being integrated into various industries:
|Region||Economic Impact of AI (2030)||% of Total|
|North America||$3.7 trillion||23.6%|
|Northern Europe||$1.8 trillion||11.5%|
|Developed Asia||$0.9 trillion||5.7%|
|Southern Europe||$0.7 trillion||4.5%|
|Latin America||$0.5 trillion||3.2%|
|Rest of World||$1.2 trillion||7.6%|
Further, the global growth from AI can be divided into two major areas, according to PwC: labor productivity improvements ($6.6 trillion) and increased consumer demand ($9.1 trillion).
Industries Most Affected
But how will AI impact industries on an individual level?
For that, we turn to Accenture’s recent report, which breaks down a similar projection of $14 trillion of gross value added (GVA) by 2035, with estimates for AI’s impact on specific industries.
|Industry||2035 GVA (Baseline)||2035 GVA (AI steady state)|
|Manufacturing||$8.4 trillion||$12.2 trillion|
|Professional Services||$7.5 trillion||$9.3 trillion|
|Wholesale & Retail||$6.2 trillion||$8.4 trillion|
|Public Services||$4.0 trillion||$4.9 trillion|
|Information & Communication||$3.7 trillion||$4.7 trillion|
|Financial Services||$3.4 trillion||$4.6 trillion|
|Construction||$2.8 trillion||$3.3 trillion|
|Transportation & Storage||$2.1 trillion||$2.9 trillion|
Manufacturing will see nearly $4 trillion in growth from AI alone – and many other industries will undergo significant changes as well.
To learn more about other tech that will have a big impact on our future, see a Timeline of Future Technology.
Assembling the World Country-by-Country, Based on Economy Size
How does the world map change if it gets assembled based on the size of economies, in ascending order of GDP or GDP per capita?
If you had to sketch a world map, you’d probably start with a place that is familiar.
Perhaps you would begin by drawing your own continent, or maybe you’d focus on the specific borders of the country you live in. Then, you’d likely move to drawing the outlines of neighboring countries, eventually working your way to far and distant lands.
This would be a logical way for anyone to think about such a task, and it gives some insight as to how humans think about the world.
We start with what’s familiar, and build it out until it’s a complete picture.
Assembling the World by Economy Size
What if we assembled a world map in a completely different order?
Today’s two animations come to us from Engaging-Data, and they approach the world map from an alternate angle: assembling countries on the map in the order of their economic footprints.
The first map, shown below, uses nominal GDP to assemble countries in ascending order:
This version of the map shows the smallest economies first, with the larger economies at the end.
For this reason, the first economies appearing on the map tend to be developing nations, or nations with smaller geographical or demographic footprints.
For example, even though the Falkland Islands are wealthy on a per capita basis, the British Overseas Territory has fewer than 4,000 people, which gives it a minor footprint on a global stage.
GDP per Capita (Nominal)
Now, let’s take a look at the same map, constructed in order of GDP per capita:
This animation is more cohesive, given that it is not dependent on population size. Instead the order here is based on economic output (in nominal terms) of the average person in each country or jurisdiction.
In this case, developing nations appear first – and at the end, more developed regions (like Europe and North America) tend to fill out.
Note: All rankings here are in nominal terms, which use market rates to calculate comparable values in U.S. dollars, while omitting the cost of living as a factor. GDP rankings change significantly when using PPP rates.
Other Ways to Assemble the World
While assembling nations based on GDP provides an interesting way to look at the world, this same approach can be tried by applying other statistics as well.
We recommend checking out this page, which allows you to “assemble the world” based on measures like population density, life expectancy, or population.
Mapped: The Salary Needed to Buy a Home in 50 U.S. Metro Areas
The annual salary needed to buy a home in the U.S. ranges from $38k to $255k, depending on the metropolitan area you are looking in.
The Salary Needed to Buy a Home in 50 U.S. Metro Areas
Over the last year, home prices have risen in 49 of the biggest 50 metro areas in the United States.
At the same time, mortgage rates have hit seven-year highs, making things more expensive for any prospective home buyer.
With this context in mind, today’s map comes from HowMuch.net, and it shows the salary needed to buy a home in the 50 largest U.S. metro areas.
The Least and Most Expensive Metro Areas
As a reference point, the median home in the United States costs about $257,600, according to the National Association of Realtors.
|Median Home Price||Montly Payment (PITI)||Salary Needed|
With a 20% down payment and a 4.90% mortgage rate, and taking into account what’s needed to pay principal, interest, taxes, and insurance (PITI) on the home, it would mean a prospective buyer would need to have $61,453.51 in salary to afford such a purchase.
However, based on your frame of reference, this national estimate may seem extremely low or quite high. That’s because the salary required to buy in different major cities in the U.S. can fall anywhere between $37,659 to $254,835.
The 10 Cheapest Metro Areas
Here are the cheapest metro areas in the U.S., based on data and calculations from HSH.com:
|Rank||Metro Area||Median Home Price||Monthly Payment (PITI)||Salary Needed|
After the dust settles, Pittsburgh ranks as the cheapest metro area in the U.S. to buy a home. According to these calculations, buying a median home in Pittsburgh – which includes the surrounding metro area – requires an annual income of less than $40,000 to buy.
Just missing the list was Detroit, where a salary of $48,002.89 is needed.
The 10 Most Expensive Metro Areas
Now, here are the priciest markets in the country, also based on data from HSH.com:
|Rank||Metro Area||Median Home Price||Monthly Payment (PITI)||Salary Needed|
|#6||New York City||$403,900||$2,465.97||$105,684.33|
Topping the list of the most expensive metro areas are San Jose and San Francisco, which are both cities fueled by the economic boom in Silicon Valley. Meanwhile, two other major metro areas in California, Los Angeles and San Diego, are not far behind.
New York City only ranks in sixth here, though it is worth noting that the NYC metro area extends well beyond the five boroughs. It includes Newark, Jersey City, and many nearby counties as well.
As a final point, it’s worth mentioning that all cities here (with the exception of Denver) are in coastal states.
Notes on Calculations
Data on median home prices comes from the National Association of Realtors and is based on 2018 Q4 information, while national mortgage rate data is derived from weekly surveys by Freddie Mac and the Mortgage Bankers Association of America for 30-year fixed rate mortgages.
Calculations include tax and homeowners insurance costs to determine the annual salary it takes to afford the base cost of owning a home (principal, interest, property tax and homeowner’s insurance, or PITI) in the nation’s 50 largest metropolitan areas.
Standard 28% “front-end” debt ratios and a 20% down payments subtracted from the median-home-price data are used to arrive at these figures.
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