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Ranked: Largest Semiconductor Foundry Companies by Revenue



This chart shows the largest semiconductor foundry companies by their percentage of global revenues in Q1 2023.

Ranked: Largest Semiconductor Foundry Companies by Revenue

They’re in our phones, cars, planes, and even fridges.

Semiconductor chips have become critical for the modern way of life, and the biggest semiconductor foundry companies rake in billions of dollars from widespread demand.

This chart shows the largest semiconductor foundry companies by their percentage of global revenues in Q1 2023, using data sourced from Trendforce.

ℹ️ We highlight data for companies that only operate foundries (fabrication plants) that manufacture chips for clients, also known as a “pure-play” foundries, as well as companies that design and manufacture their own chips, known as integrated device manufacturers. “Fabless” manufacturers that only design and don’t manufacture their own chips are not included.

Semiconductor Foundry Companies by Revenue

At the top of the list and dwarfing every other company by revenue share is TSMC which earned 60% (or nearly $17 billion) of the entire industry’s revenue in Q1 2023.

Founded in 1987, TSMC is a pure-play foundry that has become Taiwan’s largest company and manufactures products for a host of clients including Apple, NVIDIA, and AMD.

(Q1 2023, USD)
1TSMC🇹🇼 Taiwan$16,735M
2Samsung🇰🇷 South Korea$3,446M
3GlobalFoundries🇺🇸 US$1,841M
4UMC🇹🇼 Taiwan$1,784M
5SMIC🇨🇳 China$1,462M
6HuaHong Group🇨🇳 China$845M
7Tower Semiconductor🇮🇱 Israel$356M
8PSMC🇹🇼 Taiwan$332M
9VIS🇹🇼 Taiwan$269M
10DB Hitek🇰🇷 South Korea$234M
Global Total$27,860M

Note: Revenue based on the following conversion rates: USD 1 = WON 1,276; USD 1 = NTD 30.4.

Well behind TSMC in foundry revenues is integrated device manufacturer Samsung, the biggest company in South Korea, which made $3.4 billion (12.4% of the industry’s revenue) from its semiconductor manufacturing business.

GlobalFoundries from the U.S., UMC from Taiwan and SMIC from China round out the top five, with each taking home around 6% of industry’s revenue share in Q1 2023. The former spun out from AMD’s manufacturing arm when the company went fabless in 2009.

Industry concentration is apparent in semiconductors. For example, the top 10 semiconductor foundry companies account for 98% of the entire industry’s revenue. Furthermore, 90% of the market is dominated by companies in just three Asian countries: Taiwan, South Korea, and China.

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9 Problems with Generative AI, in One Chart

Generative AI tools are demonstrating massive potential to help, but also to harm. Here are 9 concerns, backed by data.





The following content is sponsored by VERSES

The contents of this article were written with the help of ChatGPT (and extensively edited and fact-checked by the Visual Capitalist editorial team).

9 Problems with Generative AI

In the rapidly evolving landscape of artificial intelligence, generative AI tools are demonstrating incredible potential. However, their potential for harm is also becoming more and more apparent.

Together with our partner VERSES, we have visualized some concerns regarding generative AI tools using data from a variety of different sources. Many of them fall into one of the following categories: quality control & data accuracy, ethical considerations, or technical challenges—with, of course, a certain degree of overlap.

Let’s dive into it.

Problem 1:

Bias In, Bias Out

Theme: Quality Control & Accuracy

One of the critical issues with generative AI lies in its tendency to reproduce biases present in the data it has been trained on. Rather than mitigating biases, these tools often magnify or perpetuate them, raising questions about the accuracy of their applications—which could lead to much bigger problems around ethics.

Problem 2:

The Black Box Problem

Theme: Ethical & Legal Considerations

Another significant hurdle in embracing generative AI is the lack of transparency in its decision-making processes. With thought processes that are often uninterpretable, these AI systems face challenges in explaining their decisions, especially when errors occur on critical matters.

It’s worth noting that this is a broader problem with AI systems and not just generative tools.

Problem 3:

High Cost to Train and Maintain

Theme: Complexity & Technical Challenges

Training generative AI models like large language model (LLM) ChatGPT is extremely expensive, with costs often reaching millions of dollars due to the computational power and infrastructure required. For instance, now Ex-CEO of OpenAI, Sam Altman confirmed that ChatGPT-4 cost a whopping $100 million to train.

Problem 4:

Mindless Parroting

Theme: Quality Control & Accuracy

Despite their advanced capabilities, generative AIs are constrained by the data and patterns they were trained on. This limitation results in outputs that may not encompass the breadth of human knowledge or address diverse scenarios.

Problem 5:

Alignment with Human Values

Theme: Ethical & Legal Considerations

Unlike humans, generative AIs lack the capacity to consider the consequences of their actions in alignment with human values.

While instances like the AI-generated “Balenciaga Pope” may appear to be harmless, it’s important to recognize that deepfakes could be employed for more harmful purposes, such as spreading false information in the face of a public health crises.

This highlights the need for more frameworks that ensure these systems operate within ethical boundaries.

Problem 6:

Power Hungry

Theme: Complexity & Technical Challenges

The environmental impact of generative AI cannot be overlooked. With processing units consuming substantial power, models like ChatGPT cost as much as powering 33,000 U.S. households, with just one inquiry being 10 to 100 times more power hungry than one email.

Problem 7:


Theme: Quality Control & Accuracy

Generative AI models have been known to create fabricated statements or images when faced with data gaps, raising concerns about the reliability of their output and potential consequences.

For example, in a Google Bard promotional video, the chatbot incorrectly asserted that the James Webb Space Telescope captured the first images of a planet beyond Earth’s solar system.

Problem 8:

Copyright & IP infringement

Theme: Ethical & Legal Considerations

The ethical use of data becomes paramount when considering that several generative AI tools appropriate copyrighted work without consent, credit, or compensation, infringing upon the rights of artists and creators.

OpenAI recently introduced a compensation program called Copyright Shield that covers legal costs for copyright infringement suits for certain customer tiers, rather than removing copyrighted material from ChatGPT’s training dataset.

Problem 9:

Static Information

Theme: Complexity & Technical Challenges

Keeping generative AI models up to date requires substantial computational resources and time, presenting a formidable technical challenge. Some models, however, are designed for incremental updates, offering a potential solution to this complex issue.


In the pursuit of harnessing the power of AI, a careful balance must be struck to ensure ethical, transparent, and impactful advancements in this transformative field.

VERSES is committed to creating intelligent software that wields transparent decision-making.

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Learn more about how VERSES is building a smarter world.

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