The US-China AI Chip War Heats Up as TSMC Halts Shipments to Chinese Companies

This week, the United States Department of Commerce (USDOC) sent a letter to Taiwan Semiconductor Manufacturing Co. (TSMC) (NASDAQ: TSM) requesting that the company suspend its shipment of advanced artificial intelligence (AI) chips to Chinese companies.

The United States Department of Commerce (USDOC) has ordered Taiwan Semiconductor Manufacturing Co. (TSMC) (NASDAQ: TSM) to halt shipments of advanced artificial intelligence (AI) chips to Chinese companies, according to a letter obtained by Reuters on Monday.

The USDOC issued this directive after discovering a TSMC chip in a Huawei AI processor, sparking concerns over international entities breaching U.S. trade restrictions. Huawei has been on the U.S. restricted trade list for several years, and any company engaging in business with Huawei must obtain a U.S. license, particularly if the technology could enhance Huawei's capabilities. If the U.S. identifies the trade or business activity as posing a threat to the United States or bolstering China's innovation or development, the license application will likely be denied.

Following the USDOC's order, TSMC notified its Chinese customers that it would suspend semiconductor shipments to them. This incident underscores the escalating tensions between the U.S. and China over technological advancements in AI.

As the world leader in AI, the U.S. is determined to protect its position and aims to restrict access to technology that could catalyze or support innovation outside its borders. The U.S. fears that advanced AI capabilities in countries like China could pose a security threat, prompting the U.S. to impose increasingly stringent controls on tech exports. As evident in the TSMC case, the U.S. governs domestic exports and exerts its influence to regulate international companies interacting with entities it deems a potential threat.

Amazon continues its foray into the AI chip market with Trainium 2, a new addition to its growing lineup of in-house AI chips, according to TechCrunch. Amazon Web Services (AWS) has stated that the company will not abandon its use of NVIDIA's (NASDAQ: NVDA) chips. However, it aims to provide its customers with a cost-efficient alternative, which Amazon hopes will entice businesses seeking to optimize their AI infrastructure with Amazon's own chips.

Amazon's focus on in-house chip production is not a recent development. In recent years, Amazon has introduced chips like Inferentia, which reportedly reduces costs by up to 40% when powering AI model responses. These potential cost savings are appealing to companies running large-scale operations where AI costs can reach millions, if not billions, annually.

Building chips internally is a strategy being adopted by several tech giants. Apple (NASDAQ: AAPL) was an early adopter of this approach, creating proprietary chips for better and cheaper integration across its devices. Recently, OpenAI announced it would follow suit when the company shared its plans to produce in-house chips.

There are numerous benefits to having a vertically integrated operation. Companies can reduce long-term costs and improve efficiency by designing chips tailored to specific operations. Customized chips can often execute tasks faster and more efficiently than general-purpose ones made by third parties, providing an advantage for companies looking to maintain a competitive edge in their specific niche.

Elon Musk's X (formerly Twitter) is rolling out a free-to-use version of its AI chatbot, Grok, making it accessible to a broader audience, reports The Verge. Grok was initially launched exclusively for Premium users, who pay a monthly fee for various perks on the social media platform, but Grok's free version is currently being tested in select countries.

However, the free version of Grok comes with limitations. Users can only make up to 10 queries every two hours with the Grok-2 model, 20 queries every two hours with the Grok-2 mini model, and ask three image analysis questions daily.

For tech giants, having a proprietary chatbot is almost an expectation—if competitors have one, they must, too. This proliferation of chatbots raises an important question: How many do we really need? While more chatbot options seem like a positive development, some users—including myself—begin to wonder why the world needs so many AI chatbots that effectively do the same thing.

Each new chatbot claims unique features, but the functionality remains largely the same for most users. Some do have specialized capabilities, but most users seem to engage with chatbots for similar tasks, which makes these slight advantages from chatbot to chatbot inconsequential.

At the moment, OpenAI's ChatGPT stands as the frontrunner in this field, and it doesn't look like it will change anytime soon, especially with the company recently raising billions of dollars at a historic valuation. However, it is only a matter of time before the other chatbots on the market either carve out their section of the legaltech market, get acquired, or cease to exist.

In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this