APRIL 2020

Why AI Chips Matter
CSET Policy Brief

AUTHOR
Saif M. KhanAs the demand for artificial intelligence applications increases and theircomplexity grows, the computational power necessary to develop anddeploy these applications will become increasingly expensive.1 Speciallydesigned “AI chips”2 fabricated using “state-of-the-art”3 technology arenecessary to achieve the combination of performance and cost-effectivenessneeded for these AI applications. The United States and its allies have astrategic advantage in state-of-the-art AI chip production that should bemaintained, if not increased.
Cutting-edge AI applications are computationally expensive,
bottlenecking progress in AI development and deployment. Rising demandfor developing AI applications—from autonomous drones to cybersecurity—
will require massively expensive amounts of computation. Since 2012, theamount of computation needed to train cutting-edge AI algorithms has
doubled every few months.4 AI lab computing costs have skyrocketed as aresult. Training the leading algorithms of Google’s DeepMind, including
AlphaGo, costs between $5 and $100 million.5 At OpenAI, computationaccounted for nearly 30 percent of costs in 2017.6 Once trained, theongoing operation of advanced AI algorithms also requires large amounts ofcomputation.7
The cost of computation is therefore a major differentiator of AI capability,
determined by state-of-the-art manufacturing technology and chip design.
AI chips made with state-of-the-art technology have two importantcharacteristics: they feature the smallest transistors and are optimized for AIspecific calculations. Such chips deliver cost-effectiveness as a result of lower
production and operational costs per calculation. Chips manufactured usingstate-of-the-art technologies are at least 10 times more cost-effective thansimilar old technology chips over the three-year life-cycle of a typical chip.
AI-specialized chip designs are an additional 10 to 1,000 more costeffective for training AI algorithms than ordinary chips,8 for a combined 100
to 10,000 times improvement in cost-effectiveness for state-of-the-art AIchips. As a result, competitiveness in AI development and deployment
requires state-of-the-art AI chips.9
The United States and a small number of allied democracies currently
dominate state-of-the-art AI chip production—a competitive advantagethat must be seized upon. State-of-the-art AI chips comprise a small
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percentage of total global chip production, and only a few factories—
representing 8.5 percent of current global chip factory capacity—can beused to manufacture them.10 If the United States and its allies maintain theircompetitive advantage in AI chip production, they can continue to lead indeveloping and fielding advanced AI applications. For these reasons, stateof-the-art AI chips—and the complex supply chains that produce them—mustbe treated as strategic assets to be maintained and grown.
Without concerted action, this U.S. and allied strategic advantage may
be time-bound. For now, the United States and its allies dominate state-ofthe-art AI chip production because the technologies needed to build suchchips belong to U.S. and allied firms. U.S. firms, for example, lead in AI chip
design; U.S., Taiwanese, and South Korean firms control essentially all of thefactories capable of producing state-of-the-art AI chips; and U.S., Dutch, andJapanese firms collectively dominate the production of semiconductor
manufacturing equipment used to fabricate chips. China is investing heavily inindigenizing its chip industry, but faces challenges, as chip technologies
represent decades of accumulated expertise and hundreds of billions ofdollars of investments by leading companies. A concerted effort by the UnitedStates and its allies to limit China’s access to these technologies could prevent
China from producing state-of-the-art AI chips for at least a decade, if notlonger.11

Acknowledgments
For helpful discussions, comments, and input, great thanks go to Carrick Flynn,Sue Gordon, Igor Mikolic-Torreira, Alexandra Vreeman, and Lynne Weil.
The author is solely responsible for all mistakes.
© 2020 Center for Security and Emerging Technology. All rights reserved.
Document Identifier: doi: 10.51593/20190015

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Endnotes
In this paper, AI refers to cutting-edge computationally-intensive AI systems, especiallydeep neural networks. Building a DNN requires “training” it to learn from a large dataset,such as a set of images. Once trained, in a process called “inference,” the DNN can classify
new pieces of data similar to those it learned about during training.
1In this paper, “AI chips” includes graphics processing units (GPUs), field-programmablegate arrays (FPGAs), and certain types of application-specific integrated circuits (ASICs)
specialized for AI calculations. “AI chips” also includes a GPU, FPGA, or AI-specific ASIC
implemented as a core on system-on-a-chip (SoC). AI algorithms can run on other types ofchips, including general-purpose chips like central processing units (CPUs), but we focus onGPUs, FPGAs, and AI-specific ASICs because of their necessity for training and running
cutting-edge AI algorithms cost-effectively and quickly.
2In this paper, “state-of-the-art” means chips with ≤16 nm transistors, which includes chips
within the four most advanced generations using up to five-year-old technology. The mostadvanced chips today have 5 nm transistors.
3

Dario Amodei and Danny Hernandez, “AI and Compute,” OpenAI, May 16, 2018,https://openai.com/blog/ai-and-compute/.
4

Jeffrey Shek, “Takeaways from OpenAI Five (2019),” Towards Data Science, April 23,
2019, https://towardsdatascience.com/takeaways-from-openai-five-2019-f90a612fe5d.
DeepMind has incurred financial losses of hundreds of millions of dollars as a result ofincreasing computing costs. Gary Marcus, “DeepMind's Losses and the Future of Artificial
Intelligence,” Wired, August 14, 2019, https://www.wired.com/story/deepminds-lossesfuture-artificial-intelligence/.
5

OpenAI, Form 990 for fiscal year ending Dec. 2017, 11,https://projects.propublica.org/nonprofits/organizations/810861541/201920719349
300822/IRS990.
6

7

Amodei et al., “AI and Compute.”

8

By “ordinary chips,” we refer to general-purpose chips such as central processing units.

Further data and details appear in CSET’s corresponding technical report: Saif M. Khanand Alexander W. Mann, “AI Chips: What They Are and Why They Matter” (Washington,
DC: Center for Security and Emerging Technology, April 2020).
910

SEMI, World Fab Forecast, May 2019 Edition.

Ideas on how the United States can leverage semiconductor supply chains appear in other
CSET reports: Saif M. Khan, “Maintaining the AI Chip Competitive Advantage of the UnitedStates and its Allies” (Washington, DC: Center for Security and Emerging Technology,December 2019), https://cset.georgetown.edu/wp-content/uploads/CSET-Maintainingthe-AI-Chip-Competitive-Advantage-of-the-United-States-and-its-Allies-20191206.pdf;
Carrick Flynn, “Recommendations on Export Controls for Artificial Intelligence” (Washington,
DC: Center for Security and Emerging Technology, February 2020),
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Center for Security and Emerging Technology | 4https://cset.georgetown.edu/wp-content/uploads/Recommendations-on-Export-Controlsfor-Artificial-Intelligence.pdf; Saif M. Khan and Carrick Flynn, “Maintaining China’s
Dependence on Democracies for Advanced Computer Chips” (Washington, DC: Center for
Security and Emerging Technology and Brookings Institution, April 2020).

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