Animated Chart: The Smartphone Effect on the Camera Market

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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 AI vs. Human Performance in Technical Tasks

AI systems have seen rapid advancements, surpassing human performance in technical tasks such as advanced math and visual reasoning.

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A line chart showing AI vs human performance in various technical tasks

AI vs. Human Performance in Technical Tasks

This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.

The gap between human and machine reasoning is narrowing—and fast.

Over the past year, AI systems have continued to see rapid advancements, surpassing human performance in technical tasks where they previously fell short, such as advanced math and visual reasoning.

This graphic visualizes AI systems’ performance relative to human baselines for eight AI benchmarks measuring tasks including:

  1. Image classification
  2. Visual reasoning
  3. Medium-level reading comprehension
  4. English language understanding
  5. Multitask language understanding
  6. Competition-level mathematics
  7. PhD-level science questions
  8. Multimodal understanding and reasoning

This visualization is part of Visual Capitalist’s AI Week, sponsored by Terzo. Data comes from the Stanford University 2025 AI Index Report.

An AI benchmark is a standardized test used to evaluate the performance and capabilities of AI systems on specific tasks.

AI Models Are Surpassing Humans in Technical Tasks

Below, we show how AI models have performed relative to the human baseline in various technical tasks in recent years.

YearPerfomance relative to the human baseline (100%)Task
201289.15%Image classification
201391.42%Image classification
201496.94%Image classification
201599.47%Image classification
2016100.74%Image classification
201680.09%Visual reasoning
2017101.37%Image classification
201782.35%Medium-level reading comprehension
201786.49%Visual reasoning
2018102.85%Image classification
201896.23%Medium-level reading comprehension
201886.70%Visual reasoning
2019103.75%Image classification
201936.08%Multitask language understanding
2019103.27%Medium-level reading comprehension
201994.21%English language understanding
201990.67%Visual reasoning
2020104.11%Image classification
202060.02%Multitask language understanding
2020103.92%Medium-level reading comprehension
202099.44%English language understanding
202091.38%Visual reasoning
2021104.34%Image classification
20217.67%Competition-level mathematics
202166.82%Multitask language understanding
2021104.15%Medium-level reading comprehension
2021101.56%English language understanding
2021102.48%Visual reasoning
2022103.98%Image classification
202257.56%Competition-level mathematics
202283.74%Multitask language understanding
2022101.67%English language understanding
2022104.36%Visual reasoning
202347.78%PhD-level science questions
202393.67%Competition-level mathematics
202396.21%Multitask language understanding
202371.91%Multimodal understanding and reasoning
2024108.00%PhD-level science questions
2024108.78%Competition-level mathematics
2024102.78%Multitask language understanding
202494.67%Multimodal understanding and reasoning
2024101.78%English language understanding

From ChatGPT to Gemini, many of the world’s leading AI models are surpassing the human baseline in a range of technical tasks.

The only task where AI systems still haven’t caught up to humans is multimodal understanding and reasoning, which involves processing and reasoning across multiple formats and disciplines, such as images, charts, and diagrams.

However, the gap is closing quickly.

In 2024, OpenAI’s o1 model scored 78.2% on MMMU, a benchmark that evaluates models on multi-discipline tasks demanding college-level subject knowledge.

This was just 4.4 percentage points below the human benchmark of 82.6%. The o1 model also has one of the lowest hallucination rates out of all AI models.

This was major jump from the end of 2023, where Google Gemini scored just 59.4%, highlighting the rapid improvement of AI performance in these technical tasks.

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