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Mapped: Interest in Generative AI by Country

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These graphics highlight interest in generative AI by country, covering interest in text, image, video, or audio generation.

Mapping Interest in Generative AI by Country

In the past two years, AI’s ability to produce text, images, audio, and video has become massively widespread.

With millions of people worldwide now embracing tools like ChatGPT and Midjourney to bring their ideas to life, billions of dollars are being invested to take AI technology to the next level.

But so far, AI interest by country varies, at least according to search data. This graphic sheds light on the countries most interested in generative AI tools using data compiled by ElectronicsHub.

 

 

Search Interest in Generative AI by Country (2023)

To determine interest in different generative AI technologies, ElectronicsHub first determined the top 10 tools in each category based on their global monthly search volumes.

They then recorded the monthly Google search volumes for each tool, combined the overall volumes of each country, and scaled the results by population (per 100,000 people) and Google’s search engine market share in each respective market.

According to the compiled data, the highest search volume for generative AI tools was seen in the Philippines (5,288) followed by Singapore (3,036) and Canada (2,213).

Let’s take a closer look at the different generative AI tools nations worldwide seem to prefer.

Generating Text with AI

Chart highlighting interest in AI text generation by country.

The launch of ChatGPT last year turned the world’s attention to the world of generative AI. However, some tools, like QuillBot, have helped users check grammar, edit, and summarize text for over five years.

Generative AI tools used for tasks ranging from drafting emails to creating job application packages have been most sought after in Asian nations like the Philippines, Singapore, Malaysia, and other parts of the world including Canada, and the UAE.

 

 

Generating Images with AI

Chart highlighting interest in AI image generation by country.

A picture speaks a thousand words. And the launch of generative Image AI tools like DALL-E 2, Midjourney, and Stable Diffusion have created a whole new world of storytelling. Once used only by designers, these text-to-image tools are now being used in science and medicine.

Israel and Singapore, the two nations leading the global interest in generative image AI, seem to prefer using Midjourney, the most-searched tool in 92 nations worldwide.

 

 

Generating Audio with AI

Chart highlighting interest in AI audio generation by country.

While the use of generative AI is not as established in audio generation, it has already begun making its mark. From its uses in voice-to-text transcription platforms to the music industry, generative AI in audio is growing worldwide.

 

 

While this may pose challenges in the music industry, tools like FakeYou and VoiceGPT, which allow mimicry of the voices of celebrities and artists, are growing increasingly popular in the South American nations of Uruguay, Chile, Argentina, and Peru in 2023.

Generating Video with AI

Chart highlighting interest in AI video generation by country.

From movies and TV shows to TikTok reels and YouTube content, videos are often the fastest way of capturing an audience’s attention. Despite this, generative AI’s video generation tools are not as developed as other generative media tools, yet.

While nations including Singapore and the UAE are searching the most for video-generation tools like InVideo and Synthesia, the world is looking for what emerges next in this field.

Generative AI is Just Starting Out

Much like how the printing press changed the way we disseminate information, generative AI is revolutionizing the way we produce and use information.

As the world of generative AI blurs the line between what’s real and what’s not, and what’s fake and what’s true, an intriguing path unfolds.

While businesses and users seek to harness the full potential of AI, governments and lawmakers are simultaneously grappling with the challenge of comprehending its regulation to curb potential drawbacks and misuse.

 

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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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Technology

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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