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Animated Chart: The Smartphone Effect on the Camera Market

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Charting the Smartphone Effect on the Camera Market

The smartphone camera has come a long way since the early 2000s, and its impact on the overall camera market cannot be understated.

In fact, modern smartphones have become so sophisticated that the CEO of Sony’s semiconductor manufacturing company predicts that smartphone cameras will soon produce better quality images than DSLR cameras.

Whether smartphones will be able to completely replace standalone cameras is still a contentious debate topic, but one thing is clear—while smartphone sales have skyrocketed over the last decade, digital camera sales have plummeted.

This animation by James Eagle compares annual sales data for film cameras, digital cameras, and smartphones over the years to show just how much smartphones have impacted the camera market.

A (Brief) History of Standalone Cameras

Below, we’ve broken down the history of cameras into three overarching periods: early cameras, film cameras, and digital cameras.

Early Cameras

Cameras have been around for thousands of years, with descriptions of camera-like devices found in historical writings dating back as far as the 4th century:

  • 330 AD: Ancient Chinese texts describe a device known as a camera obscura. Similar to pinhole cameras, these didn’t produce actual photographs, but rather reflected light onto screens which could then be traced to produce a lasting image.
  • Early 1800s: It’s generally accepted that Joseph Nicéphore Niépce invented the first photographic camera in 1816. Using silver chloride, Niépce managed to develop an image that’s still around today.
  • 1840s: Early cameras produced negative images which had to be color corrected, until mirrored cameras were invented. Alexander S. Wolcott was the first person to patent a mirrored camera in 1840.
  • 1871: Richard Leach Maddox came up with an invention that led to instantaneous exposure, meaning cameras only needed to be exposed to light for a few seconds before producing an image.

These early inventions were critical milestones in the development of the modern-day camera. However, cameras and film weren’t available to the masses until Kodak’s Brownie camera made photography relatively cheap.

The Emergence of Film Cameras

Released in 1900, the Kodak Brownie was a handheld, inexpensive roll film camera invented by George Eastman.

When it first launched, the camera sold for $1.00, equivalent to about $35.48 in 2022 dollars. With more than 100,000 cameras sold within the first year, Eastman is often credited for making photography accessible to the masses.

Fast forward a few decades, and technological advancements led to features in cameras like viewfinders, different shutter speeds, and detachable lenses. These features were possible on what’s known as twin lens reflex cameras, or TLR for short, but they were soon replaced by single lens reflex cameras (SLR).

Digital Cameras Enter the Scene

By the late 1990s, digital cameras were invented and began quickly outselling film cameras.

Unlike their film counterparts, digital cameras feature a digital sensor, and store images on a memory card which could store thousands of pictures.

Digital camera sales grew throughout the early 2000s—in 2005 the Photo Marketing Association International even estimated that 52% of households would own a digital camera by the end of the year.

The Smartphone Camera Changes the Game

In the early 2000s, camera phones were far less powerful than their standalone counterparts.

For instance, one of the first camera phones to hit the market, Samsung’s SCH-V200, could take 20 pictures at 0.35-megapixel resolution. In contrast, Canon’s EOS D30 digital camera released the same year had a resolution of 3 megapixels.

But the advent of the iPhone, and the rollout and accessibility of modern smartphones with powerful cameras, quickly saw many non-enthusiasts switch to smartphone cameras only. In 2022, Google’s Pixel 7 has multiple built-in cameras, with both a 50 megapixel wide rear camera and a 12 megapixel ultrawide rear camera. In comparison, Canon’s ​​enthusiast EOS 850 has a 24.1 megapixel sensor.

The animated chart above highlights the direct impact on the digital camera market after its 2009/2010 peak:

YearDigital Camera Sales
199910.2 million
2009121.2 million
20218.4 million

So does that make a modern smartphone camera better? Not at all, as there are other a multitude of factors to consider when assessing a camera’s quality besides resolution. But in an article in Wired Magazine, tech journalist Sam Kieldsen explains how the market has shifted:

[Smartphones have] effectively killed off the cheap pocket point-and-shoot camera already, but there’s still so much they can’t do in comparison to a true purpose-built mirrorless or DSLR camera. Low light image quality, convincing bokeh effects and extreme close-up macro photography are all still significantly better on a real camera.

Smartphones may not be fully replacing DSLR cameras anytime soon, but they’ve certainly changed the industry and game in which it plays.

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This article was published as a part of Visual Capitalist's Creator Program, which features data-driven visuals from some of our favorite Creators around the world.

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Visualizing the Top U.S. States for AI Jobs

Nearly 800,000 AI jobs were posted in the U.S. throughout 2022. View this graphic to see a breakdown by state.

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Visualizing the Top U.S. States for AI Jobs

Much ink has been spilled over fears that artificial intelligence (AI) will eliminate jobs in the economy. While some of those fears may be well-founded, red-hot interest in AI innovation is creating new jobs as well.

This graphic visualizes data from Lightcast, a labor market analytics firm, which shows how many AI-related jobs were posted in each state throughout 2022.

In total there were 795,624 AI jobs posted throughout the year, of which 469,925 (59%) were in the top 10. The full tally is included in the table below.

RankStateNumber of job postings% of total
1California142,15417.9%
2Texas66,6248.4%
3New York43,8995.5%
4Massachusetts34,6034.3%
5Virginia34,2214.3%
6Florida33,5854.2%
7Illinois31,5694.0%
8Washington31,2843.9%
9Georgia26,6203.3%
10Michigan25,3663.2%
11North Carolina23,8543.0%
12New Jersey23,4472.9%
13Colorado20,4212.6%
14Pennsylvania20,3972.6%
15Arizona19,5142.5%
16Ohio19,2082.4%
17Maryland16,7692.1%
18Minnesota11,8081.5%
19Tennessee11,1731.4%
20Missouri10,9901.4%
21Oregon10,8111.4%
22Washington, D.C.9,6061.2%
23Indiana9,2471.2%
24Connecticut8,9601.1%
25Wisconsin8,8791.1%
26Alabama7,8661.0%
27Kansas7,6831.0%
28Arkansas7,2470.9%
29Utah6,8850.9%
30Nevada6,8130.9%
31Idaho6,1090.8%
32Oklahoma5,7190.7%
33Iowa5,6700.7%
34South Carolina4,9280.6%
35Louisiana4,8060.6%
36Kentucky4,5360.6%
37Nebraska4,0320.5%
38Delaware3,5030.4%
39New Mexico3,3570.4%
40Rhode Island2,9650.4%
41New Hampshire2,7190.3%
42Hawaii2,5500.3%
43Mississippi2,5480.3%
44Maine2,2270.3%
45South Dakota2,1950.3%
46Vermont1,5710.2%
47North Dakota1,2270.2%
48Alaska9700.1%
49West Virginia8870.1%
50Montana8330.1%
51Wyoming7690.1%

The following chart adds some context to these numbers. It shows how the percentage of AI job postings in some of the top states has changed since 2010.

We can see that California quickly became the primary destination for AI jobs in the early 2010s, presumably as Silicon Valley companies began developing the technology.

California’s share has since declined, with a significant number of jobs seemingly moving to Texas. In fact, many tech companies are relocating to Texas to avoid California’s relatively higher taxes and cost of living.

The 10 Most In-Demand Specialized Skills

Lightcast also captured the top 10 specialized skills that were required for AI-related jobs. These are listed in the table below.

SkillFrequency (number of postings)Frequency (% of postings)
Python296,66237%
Computer Science260,33333%
SQL185,80723%
Data Analysis159,80120%
Data Science157,85520%
Amazon Web Services155,61519%
Agile Methodology152,96519%
Automation138,79117%
Java133,85617%
Software Engineering133,28617%

If you’re interested in a career that focuses on AI, becoming proficient in Python is likely to be a good first step.

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